{
 "cells": [
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   "cell_type": "markdown",
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   "source": [
    "# High-Dimensional Time Series Forecasting with Convolutional Neural Networks: Adding Exogenous Features to WaveNet\n",
    "\n",
    "**Note**: for a written overview on this topic, check out my blog post (coming soon!!). \n",
    "\n",
    "This notebook builds on the [previous notebook in this series](https://github.com/JEddy92/TimeSeries_Seq2Seq/blob/master/notebooks/TS_Seq2Seq_Conv_Full.ipynb), where I demonstrated in python/keras code how a **convolutional** sequence-to-sequence neural network modeled after WaveNet can be built for the purpose of high-dimensional time series forecasting. I assume that you're comfortable with the core model and set out to improve it by adding **exogenous features** to the model's input on top of the raw time series signals. In forecasting, exogenous features are those external to the forecasted series that may have a causal influence on it (e.g. day of the week or language of a wikipedia page). They often provide predictive signal that's not fully captured by historical values of the target series alone. Here we'll see how these features can be derived and properly formatted in a keras setting along with how our network architecture can be modified to handle them.   \n",
    "\n",
    "We'll be using the same daily wikipedia web page traffic dataset, available [here on Kaggle](https://www.kaggle.com/c/web-traffic-time-series-forecasting/data). And once again with our full-fledged model we'll forecast 60 days into the future, using all of the series history available in \"train_1.csv\" for the encoding stage of the model. \n",
    "\n",
    "Our workflow's structure is mostly unchanged from the previous notebook, but adds a new section on exogenous feature extraction. The features we'll extract are day of the week and page-specific metadata (language, access type, and agent type). The model formatting and building sections are also modified to accomodate the inclusion of these features. Feel free to focus on those sections (2-6) if you're comfortable with the setup steps (as in the previous notebooks).     \n",
    "\n",
    "Here's a section breakdown of this notebook -- enjoy!\n",
    "\n",
    "**1. Loading and Previewing the Data**   \n",
    "**2. Exogenous Feature Engineering**   \n",
    "**3. Formatting the Data for Modeling**  \n",
    "**4. Building the Model - Training Architecture**  \n",
    "**5. Building the Model - Inference Loop**  \n",
    "**6. Generating and Plotting Predictions**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Loading and Previewing the Data \n",
    "\n",
    "First thing's first, let's load up the data and get a quick feel for it (reminder that the dataset is available [here](https://www.kaggle.com/c/web-traffic-time-series-forecasting/data)). \n",
    "\n",
    "Note that there are a good number of NaN values in the data that don't disambiguate missing from zero. For the sake of simplicity in this tutorial, we'll naively fill these with 0 later on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2015-07-04</th>\n",
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       "      <th>2015-07-08</th>\n",
       "      <th>2015-07-09</th>\n",
       "      <th>...</th>\n",
       "      <th>2016-12-22</th>\n",
       "      <th>2016-12-23</th>\n",
       "      <th>2016-12-24</th>\n",
       "      <th>2016-12-25</th>\n",
       "      <th>2016-12-26</th>\n",
       "      <th>2016-12-27</th>\n",
       "      <th>2016-12-28</th>\n",
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       "      <th>3</th>\n",
       "      <td>4minute_zh.wikipedia.org_all-access_spider</td>\n",
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       "                                                Page  2015-07-01  2015-07-02  \\\n",
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       "3         4minute_zh.wikipedia.org_all-access_spider        35.0        13.0   \n",
       "4  52_Hz_I_Love_You_zh.wikipedia.org_all-access_s...         NaN         NaN   \n",
       "\n",
       "   2015-07-03  2015-07-04  2015-07-05  2015-07-06  2015-07-07  2015-07-08  \\\n",
       "0         5.0        13.0        14.0         9.0         9.0        22.0   \n",
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       "3        10.0        94.0         4.0        26.0        14.0         9.0   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   2015-07-09     ...      2016-12-22  2016-12-23  2016-12-24  2016-12-25  \\\n",
       "0        26.0     ...            32.0        63.0        15.0        26.0   \n",
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       "2         4.0     ...             3.0         1.0         1.0         7.0   \n",
       "3        11.0     ...            32.0        10.0        26.0        27.0   \n",
       "4         NaN     ...            48.0         9.0        25.0        13.0   \n",
       "\n",
       "   2016-12-26  2016-12-27  2016-12-28  2016-12-29  2016-12-30  2016-12-31  \n",
       "0        14.0        20.0        22.0        19.0        18.0        20.0  \n",
       "1         9.0        30.0        52.0        45.0        26.0        20.0  \n",
       "2         4.0         4.0         6.0         3.0         4.0        17.0  \n",
       "3        16.0        11.0        17.0        19.0        10.0        11.0  \n",
       "4         3.0        11.0        27.0        13.0        36.0        10.0  \n",
       "\n",
       "[5 rows x 551 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "\n",
    "df = pd.read_csv('../data/train_1.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 145063 entries, 0 to 145062\n",
      "Columns: 551 entries, Page to 2016-12-31\n",
      "dtypes: float64(550), object(1)\n",
      "memory usage: 609.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data ranges from 2015-07-01 to 2016-12-31\n"
     ]
    }
   ],
   "source": [
    "data_start_date = df.columns[1]\n",
    "data_end_date = df.columns[-1]\n",
    "print('Data ranges from %s to %s' % (data_start_date, data_end_date))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can define a function that lets us visualize some random webpage series as below. For the sake of smoothing out the scale of traffic across different series, we apply a log1p transformation before plotting - i.e. take $\\log(1+x)$ for each value $x$ in a series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_random_series(df, n_series):\n",
    "    \n",
    "    sample = df.sample(n_series, random_state=8)\n",
    "    page_labels = sample['Page'].tolist()\n",
    "    series_samples = sample.loc[:,data_start_date:data_end_date]\n",
    "    \n",
    "    plt.figure(figsize=(10,6))\n",
    "    \n",
    "    for i in range(series_samples.shape[0]):\n",
    "        np.log1p(pd.Series(series_samples.iloc[i]).astype(np.float64)).plot(linewidth=1.5)\n",
    "    \n",
    "    plt.title('Randomly Selected Wikipedia Page Daily Views Over Time (Log(views) + 1)')\n",
    "    plt.legend(page_labels)\n",
    "    \n",
    "plot_random_series(df, 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Exogenous Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Glancing back at our preview of the data above, we see that we have information that isn't directly captured by the raw traffic time series. The **page column** gives us metadata on each series that might guide our model in identifying shared patterns across related series. For example, pages written in the same language may exhibit similar seasonality patterns. Similarly, the **date column headers** let us explicitly encode day of the week information as a way of anchoring the model's understanding of weekly seasonality. In the code to follow, we'll extract numeric features from both of these sources and wrangle them into a format that keras will cleanly accept as input when combined with the raw series.\n",
    "\n",
    "\n",
    "\n",
    "![architecture](images/Page_exog.png)\n",
    "\n",
    "\n",
    "\n",
    "Let's start by converting dates to **one-hot-encoded** / **dummy variable** representations of day of the week, following the standard approach for handling categorical features. It's simple to do this using pandas:  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0  1  2  3  4  5  6\n",
       "0  0  0  1  0  0  0  0\n",
       "1  0  0  0  1  0  0  0\n",
       "2  0  0  0  0  1  0  0\n",
       "3  0  0  0  0  0  1  0\n",
       "4  0  0  0  0  0  0  1\n",
       "5  1  0  0  0  0  0  0\n",
       "6  0  1  0  0  0  0  0\n",
       "7  0  0  1  0  0  0  0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dow_ohe = pd.get_dummies(pd.to_datetime(df.columns[1:]).dayofweek)\n",
    "dow_ohe.head(8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Right now we have a dataframe of shape (n_timesteps, 7), with binary columns corresponding to each day of the week. We need to do a bit more for keras-friendly formatting: when processing a sequence of features, keras expects input arrays (tensors) of shape **(n_samples, n_timesteps, n_features)**. In this case, the day of week features are shared across all of the individual page series (our samples), so we want to just repeat the one-hot-encoded data n_samples (~145,000) times to get the 3-dimensional array we need. We can accomplish this using the handy numpy functions **expand_dims** and **tile** as below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(145063, 550, 7)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dow_array = np.expand_dims(dow_ohe.values, axis=0) # add sample dimension\n",
    "dow_array = np.tile(dow_array,(df.shape[0],1,1)) # repeat OHE array along sample dimension\n",
    "dow_array.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great, that's the exact format we need for an input array; now we'll just add the **page metadata** to this array. Let's take a quick look at the raw format of that metadata:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0              2NE1_zh.wikipedia.org_all-access_spider\n",
       "1               2PM_zh.wikipedia.org_all-access_spider\n",
       "2                3C_zh.wikipedia.org_all-access_spider\n",
       "3           4minute_zh.wikipedia.org_all-access_spider\n",
       "4    52_Hz_I_Love_You_zh.wikipedia.org_all-access_s...\n",
       "5              5566_zh.wikipedia.org_all-access_spider\n",
       "6            91Days_zh.wikipedia.org_all-access_spider\n",
       "7             A'N'D_zh.wikipedia.org_all-access_spider\n",
       "Name: Page, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Page'].head(8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It turns out that this data is underscore delimited as **name_project_access_agent**. We should split that out into distinct columns, with the one tricky part being that underscores can occur in the _name_ field that would throw off a simple split on underscore. A quick workaround is to use the **rsplit** function to work from right to left and limit the number of splits to 3. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>project</th>\n",
       "      <th>access</th>\n",
       "      <th>agent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2NE1</td>\n",
       "      <td>zh.wikipedia.org</td>\n",
       "      <td>all-access</td>\n",
       "      <td>spider</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2PM</td>\n",
       "      <td>zh.wikipedia.org</td>\n",
       "      <td>all-access</td>\n",
       "      <td>spider</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3C</td>\n",
       "      <td>zh.wikipedia.org</td>\n",
       "      <td>all-access</td>\n",
       "      <td>spider</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4minute</td>\n",
       "      <td>zh.wikipedia.org</td>\n",
       "      <td>all-access</td>\n",
       "      <td>spider</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>52_Hz_I_Love_You</td>\n",
       "      <td>zh.wikipedia.org</td>\n",
       "      <td>all-access</td>\n",
       "      <td>spider</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               name           project      access   agent\n",
       "0              2NE1  zh.wikipedia.org  all-access  spider\n",
       "1               2PM  zh.wikipedia.org  all-access  spider\n",
       "2                3C  zh.wikipedia.org  all-access  spider\n",
       "3           4minute  zh.wikipedia.org  all-access  spider\n",
       "4  52_Hz_I_Love_You  zh.wikipedia.org  all-access  spider"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "page_df = df['Page'].str.rsplit('_', n=3, expand=True) # split page string and expand to multiple columns \n",
    "page_df.columns = ['name','project','access','agent']\n",
    "page_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That looks great, so let's do a quick cardinality check on each of these categorical variables before we one-hot-encode them like we did for day of the week."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name       49174\n",
       "project        9\n",
       "access         3\n",
       "agent          2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "page_df.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since there are so many different names, it's likely more trouble than it's worth to include name in our feature set (it would mean an increase of ~50000 features in our input array!). So we'll drop name and one-hot-encode the low cardinality features to get a dataframe of shape (n_samples, n_features). Then we can repeat these features along the timesteps dimension to get the needed 3-dimensional array.      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(145063, 550, 14)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "page_df = page_df.drop('name', axis=1)\n",
    "\n",
    "page_array = pd.get_dummies(page_df).values\n",
    "page_array = np.expand_dims(page_array, axis=1) # add timesteps dimension\n",
    "page_array = np.tile(page_array,(1,dow_array.shape[1],1)) # repeat OHE array along timesteps dimension \n",
    "page_array.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The final step to complete our exogenous feature array is simply to concatenate the day of week information and page metadata into one shared array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(145063, 550, 21)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exog_array = np.concatenate([dow_array, page_array], axis=-1)\n",
    "exog_array.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's it, our exogenous feature extraction is all done! In the next section we'll write a function that lets us combine this exogenous feature array with the endogenous time series data in order to prepare for model training and prediction.   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Formatting the Data for Modeling \n",
    "\n",
    "Sadly we can't just throw the time series dataframe and exogenous array we've created into keras and let it work its magic. Instead, we have to set up a few more data transformation steps to extract the exact numpy arrays that we'll later pass to keras. But even before doing that, we have to know how to appropriately partition the time series into encoding and prediction intervals for the purposes of training and validation. Note that for our simple convolutional model we won't use an encoder-decoder architecture like in the first notebook in this repo, but **we'll keep the \"encoding\" and \"decoding\" (prediction) terminology to be consistent** -- in this case, the encoding interval represents the entire series history that we will use for the network's feature learning, but not output any predictions on. \n",
    "\n",
    "We'll use a style of **walk-forward validation**, where our validation set spans the same time-range as our training set, but shifted forward in time (in this case by 60 days). This way, we simulate how our model will perform on unseen data that comes in the future. \n",
    "\n",
    "[Artur Suilin](https://github.com/Arturus/kaggle-web-traffic/blob/master/how_it_works.md) has created a very nice image that visualizes this validation style and contrasts it with traditional validation. I highly recommend checking out his entire repo, as he's implemented a truly state of the art (and competition winning) seq2seq model on this data set. \n",
    "\n",
    "![architecture](images/ArturSuilin_validation.png)\n",
    "\n",
    "### Train and Validation Series Partioning\n",
    "\n",
    "We need to create 4 sub-segments of the data:\n",
    "\n",
    "    1. Train encoding period\n",
    "    2. Train decoding period (train targets, 60 days)\n",
    "    3. Validation encoding period\n",
    "    4. Validation decoding period (validation targets, 60 days)\n",
    "    \n",
    "We'll do this by finding the appropriate start and end dates for each segment. Starting from the end of the data we've loaded, we'll work backwards to get validation and training prediction intervals. Then we'll work forward from the start to get training and validation encoding intervals. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from datetime import timedelta\n",
    "\n",
    "pred_steps = 60 \n",
    "pred_length=timedelta(pred_steps)\n",
    "\n",
    "first_day = pd.to_datetime(data_start_date) \n",
    "last_day = pd.to_datetime(data_end_date)\n",
    "\n",
    "val_pred_start = last_day - pred_length + timedelta(1)\n",
    "val_pred_end = last_day\n",
    "\n",
    "train_pred_start = val_pred_start - pred_length\n",
    "train_pred_end = val_pred_start - timedelta(days=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "enc_length = train_pred_start - first_day\n",
    "\n",
    "train_enc_start = first_day\n",
    "train_enc_end = train_enc_start + enc_length - timedelta(1)\n",
    "\n",
    "val_enc_start = train_enc_start + pred_length\n",
    "val_enc_end = val_enc_start + enc_length - timedelta(1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train encoding: 2015-07-01 00:00:00 - 2016-09-02 00:00:00\n",
      "Train prediction: 2016-09-03 00:00:00 - 2016-11-01 00:00:00 \n",
      "\n",
      "Val encoding: 2015-08-30 00:00:00 - 2016-11-01 00:00:00\n",
      "Val prediction: 2016-11-02 00:00:00 - 2016-12-31 00:00:00\n",
      "\n",
      "Encoding interval: 430\n",
      "Prediction interval: 60\n"
     ]
    }
   ],
   "source": [
    "print('Train encoding:', train_enc_start, '-', train_enc_end)\n",
    "print('Train prediction:', train_pred_start, '-', train_pred_end, '\\n')\n",
    "print('Val encoding:', val_enc_start, '-', val_enc_end)\n",
    "print('Val prediction:', val_pred_start, '-', val_pred_end)\n",
    "\n",
    "print('\\nEncoding interval:', enc_length.days)\n",
    "print('Prediction interval:', pred_length.days)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Keras Data Formatting\n",
    "\n",
    "Now that we have the time segment dates, we'll define the functions we need to extract the data in keras friendly format. Here are the steps:\n",
    "\n",
    "* Pull the time series into an array, save a date_to_index mapping as a utility for referencing into the array \n",
    "* Create function to extract specified time interval from all the series \n",
    "* Create functions to transform all the series. \n",
    "    - Here we smooth out the scale by taking log1p and de-meaning each series using the encoder series mean, then reshape to the **(n_series, n_timesteps, n_features) tensor format** that keras will expect. \n",
    "    - Note that if we want to generate true predictions instead of log scale ones, we can easily apply a reverse transformation at prediction time. \n",
    "* Create final function to extract complete encoding and target arrays, leveraging prior functions \n",
    "    - This will act as a one-shot function that grabs what we need to train or predict\n",
    "    - It will extract (transformed) endogenous series data and combine it with our exogenous features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first code block below accomplishes the first 3 steps, unchanged from the earlier notebooks in this series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "date_to_index = pd.Series(index=pd.Index([pd.to_datetime(c) for c in df.columns[1:]]),\n",
    "                          data=[i for i in range(len(df.columns[1:]))])\n",
    "\n",
    "series_array = df[df.columns[1:]].values\n",
    "\n",
    "def get_time_block_series(series_array, date_to_index, start_date, end_date):\n",
    "    \n",
    "    inds = date_to_index[start_date:end_date]\n",
    "    return series_array[:,inds]\n",
    "\n",
    "def transform_series_encode(series_array):\n",
    "    \n",
    "    series_array = np.log1p(np.nan_to_num(series_array)) # filling NaN with 0\n",
    "    series_mean = series_array.mean(axis=1).reshape(-1,1) \n",
    "    series_array = series_array - series_mean\n",
    "    series_array = series_array.reshape((series_array.shape[0],series_array.shape[1], 1))\n",
    "    \n",
    "    return series_array, series_mean\n",
    "\n",
    "def transform_series_decode(series_array, encode_series_mean):\n",
    "    \n",
    "    series_array = np.log1p(np.nan_to_num(series_array)) # filling NaN with 0\n",
    "    series_array = series_array - encode_series_mean\n",
    "    series_array = series_array.reshape((series_array.shape[0],series_array.shape[1], 1))\n",
    "    \n",
    "    return series_array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can leverage the first 3 processing steps built out above in order to create a one-shot preprocessing function for extracting encoder/input data (with the correct exogenous features attached) and decoder/target data. We'll include arguments that let us choose the number of time series samples to extract and which periods to sample from. With this function written, we'll be ready to set up the model!      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_data_encode_decode(series_array, exog_array, first_n_samples,\n",
    "                           date_to_index, enc_start, enc_end, pred_start, pred_end):\n",
    "\n",
    "    exog_inds = date_to_index[enc_start:pred_end]\n",
    "    \n",
    "    # sample of series from enc_start to enc_end  \n",
    "    encoder_input_data = get_time_block_series(series_array, date_to_index, \n",
    "                                               enc_start, enc_end)[:first_n_samples]\n",
    "    encoder_input_data, encode_series_mean = transform_series_encode(encoder_input_data)\n",
    "    \n",
    "    # sample of series from pred_start to pred_end \n",
    "    decoder_target_data = get_time_block_series(series_array, date_to_index, \n",
    "                                                pred_start, pred_end)[:first_n_samples]\n",
    "    decoder_target_data = transform_series_decode(decoder_target_data, encode_series_mean)\n",
    "    \n",
    "    # we append a lagged history of the target series to the input data, \n",
    "    # so that we can train with teacher forcing\n",
    "    lagged_target_history = decoder_target_data[:,:-1,:1]\n",
    "    encoder_input_data = np.concatenate([encoder_input_data, lagged_target_history], axis=1)\n",
    "    \n",
    "    # we add the exogenous features corresponding to day after input series\n",
    "    # values to the input data (exog should match day we are predicting)\n",
    "    exog_input_data = exog_array[:first_n_samples,exog_inds,:][:,1:,:]\n",
    "    encoder_input_data = np.concatenate([encoder_input_data, exog_input_data], axis=-1)\n",
    "    \n",
    "    return encoder_input_data, decoder_target_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Building the Model - Architecture\n",
    "\n",
    "This convolutional architecture is a full-fledged version of the [WaveNet model](https://deepmind.com/blog/wavenet-generative-model-raw-audio/), designed as a generative model for audio (in particular, for text-to-speech applications). The wavenet model can be abstracted beyond audio to apply to any time series forecasting problem, providing a nice structure for capturing long-term dependencies without an excessive number of learned weights. Exogenous features can be integrated into WaveNet simply by extending the 3rd dimension (feature dimension) of the tensors that we feed to the model.\n",
    "\n",
    "The core of the wavenet model can be described as a **stack of residual blocks** that utilize **dilated causal convolutions**, visualized by the two diagrams from the wavenet paper below. I've gone into detailed discussion of these model components in the two previous notebooks of this series ([part 1](https://github.com/JEddy92/TimeSeries_Seq2Seq/blob/master/notebooks/TS_Seq2Seq_Conv_Intro.ipynb), [part 2](https://github.com/JEddy92/TimeSeries_Seq2Seq/blob/master/notebooks/TS_Seq2Seq_Conv_Full.ipynb)), so I'd recommend checking those out if you want to build familiarity.\n",
    "\n",
    "![dilatedconv](images/WaveNet_dilatedconv.png)  \n",
    "\n",
    "![blocks](images/WaveNet_residblock.png)        \n",
    "\n",
    "\n",
    "### **Our Architecture**\n",
    "\n",
    "With all of our components now laid out, here's what we'll use:\n",
    "\n",
    "* 16 dilated causal convolutional blocks\n",
    "    * Preprocessing and postprocessing (time distributed) fully connected layers (convolutions with filter width 1): 32 output units\n",
    "    * 32 filters of width 2 per block\n",
    "    * Exponentially increasing dilation rate with a reset (1, 2, 4, 8, ..., 128, 1, 2, ..., 128) \n",
    "    * Gated activations\n",
    "    * Residual and skip connections\n",
    "* 2 (time distributed) fully connected layers to map sum of skip outputs to final output \n",
    "\n",
    "Note that the only change in architecture from the previous notebook (without exogenous features) is an increase in units from 16 to 32 for the pre and postprocessing layers. This increase lets us better handle the larger number of input features (before we only used 1 feature!). \n",
    "\n",
    "As in the previous notebook, we'll extract the last 60 steps from the output sequence as our predicted output for training. We'll also use teacher forcing again during training, and write a separate function for iterative inference (section 5). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "tf.logging.set_verbosity(tf.logging.FATAL) # suppress unhelpful tf warnings\n",
    "\n",
    "from keras.models import Model\n",
    "from keras.layers import Input, Conv1D, Dense, Activation, Dropout, Lambda, Multiply, Add, Concatenate\n",
    "from keras.optimizers import Adam\n",
    "\n",
    "# convolutional operation parameters\n",
    "n_filters = 32 # 32 \n",
    "filter_width = 2\n",
    "dilation_rates = [2**i for i in range(8)] * 2 \n",
    "\n",
    "# define an input history series and pass it through a stack of dilated causal convolution blocks. \n",
    "# Note the feature input dimension corresponds to the raw series and all exogenous features  \n",
    "history_seq = Input(shape=(None, 1 + exog_array.shape[-1]))\n",
    "x = history_seq\n",
    "\n",
    "skips = []\n",
    "for dilation_rate in dilation_rates:\n",
    "    \n",
    "    # preprocessing - equivalent to time-distributed dense\n",
    "    x = Conv1D(32, 1, padding='same', activation='relu')(x) \n",
    "    \n",
    "    # filter convolution\n",
    "    x_f = Conv1D(filters=n_filters,\n",
    "                 kernel_size=filter_width, \n",
    "                 padding='causal',\n",
    "                 dilation_rate=dilation_rate)(x)\n",
    "    \n",
    "    # gating convolution\n",
    "    x_g = Conv1D(filters=n_filters,\n",
    "                 kernel_size=filter_width, \n",
    "                 padding='causal',\n",
    "                 dilation_rate=dilation_rate)(x)\n",
    "    \n",
    "    # multiply filter and gating branches\n",
    "    z = Multiply()([Activation('tanh')(x_f),\n",
    "                    Activation('sigmoid')(x_g)])\n",
    "    \n",
    "    # postprocessing - equivalent to time-distributed dense\n",
    "    z = Conv1D(32, 1, padding='same', activation='relu')(z)\n",
    "    \n",
    "    # residual connection\n",
    "    x = Add()([x, z])    \n",
    "    \n",
    "    # collect skip connections\n",
    "    skips.append(z)\n",
    "\n",
    "# add all skip connection outputs \n",
    "out = Activation('relu')(Add()(skips))\n",
    "\n",
    "# final time-distributed dense layers \n",
    "out = Conv1D(128, 1, padding='same')(out)\n",
    "out = Activation('relu')(out)\n",
    "out = Dropout(.2)(out)\n",
    "out = Conv1D(1, 1, padding='same')(out)\n",
    "\n",
    "# extract the last 60 time steps as the training target\n",
    "def slice(x, seq_length):\n",
    "    return x[:,-seq_length:,:]\n",
    "\n",
    "pred_seq_train = Lambda(slice, arguments={'seq_length':60})(out)\n",
    "\n",
    "model = Model(history_seq, pred_seq_train)\n",
    "model.compile(Adam(), loss='mean_absolute_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, None, 22)     0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_1 (Conv1D)               (None, None, 32)     736         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_2 (Conv1D)               (None, None, 32)     2080        conv1d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_3 (Conv1D)               (None, None, 32)     2080        conv1d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, None, 32)     0           conv1d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, None, 32)     0           conv1d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "multiply_1 (Multiply)           (None, None, 32)     0           activation_1[0][0]               \n",
      "                                                                 activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_4 (Conv1D)               (None, None, 32)     1056        multiply_1[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, None, 32)     0           conv1d_1[0][0]                   \n",
      "                                                                 conv1d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_5 (Conv1D)               (None, None, 32)     1056        add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_6 (Conv1D)               (None, None, 32)     2080        conv1d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_7 (Conv1D)               (None, None, 32)     2080        conv1d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, None, 32)     0           conv1d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, None, 32)     0           conv1d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "multiply_2 (Multiply)           (None, None, 32)     0           activation_3[0][0]               \n",
      "                                                                 activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_8 (Conv1D)               (None, None, 32)     1056        multiply_2[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, None, 32)     0           conv1d_5[0][0]                   \n",
      "                                                                 conv1d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_9 (Conv1D)               (None, None, 32)     1056        add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_10 (Conv1D)              (None, None, 32)     2080        conv1d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_11 (Conv1D)              (None, None, 32)     2080        conv1d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, None, 32)     0           conv1d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, None, 32)     0           conv1d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "multiply_3 (Multiply)           (None, None, 32)     0           activation_5[0][0]               \n",
      "                                                                 activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_12 (Conv1D)              (None, None, 32)     1056        multiply_3[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     (None, None, 32)     0           conv1d_9[0][0]                   \n",
      "                                                                 conv1d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_13 (Conv1D)              (None, None, 32)     1056        add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_14 (Conv1D)              (None, None, 32)     2080        conv1d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_15 (Conv1D)              (None, None, 32)     2080        conv1d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, None, 32)     0           conv1d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, None, 32)     0           conv1d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "multiply_4 (Multiply)           (None, None, 32)     0           activation_7[0][0]               \n",
      "                                                                 activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_16 (Conv1D)              (None, None, 32)     1056        multiply_4[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "add_4 (Add)                     (None, None, 32)     0           conv1d_13[0][0]                  \n",
      "                                                                 conv1d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_17 (Conv1D)              (None, None, 32)     1056        add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_18 (Conv1D)              (None, None, 32)     2080        conv1d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_19 (Conv1D)              (None, None, 32)     2080        conv1d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, None, 32)     0           conv1d_18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, None, 32)     0           conv1d_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "multiply_5 (Multiply)           (None, None, 32)     0           activation_9[0][0]               \n",
      "                                                                 activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_20 (Conv1D)              (None, None, 32)     1056        multiply_5[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "add_5 (Add)                     (None, None, 32)     0           conv1d_17[0][0]                  \n",
      "                                                                 conv1d_20[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_21 (Conv1D)              (None, None, 32)     1056        add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_22 (Conv1D)              (None, None, 32)     2080        conv1d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_23 (Conv1D)              (None, None, 32)     2080        conv1d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, None, 32)     0           conv1d_22[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, None, 32)     0           conv1d_23[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "multiply_6 (Multiply)           (None, None, 32)     0           activation_11[0][0]              \n",
      "                                                                 activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_24 (Conv1D)              (None, None, 32)     1056        multiply_6[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "add_6 (Add)                     (None, None, 32)     0           conv1d_21[0][0]                  \n",
      "                                                                 conv1d_24[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_25 (Conv1D)              (None, None, 32)     1056        add_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_26 (Conv1D)              (None, None, 32)     2080        conv1d_25[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_27 (Conv1D)              (None, None, 32)     2080        conv1d_25[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, None, 32)     0           conv1d_26[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, None, 32)     0           conv1d_27[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "multiply_7 (Multiply)           (None, None, 32)     0           activation_13[0][0]              \n",
      "                                                                 activation_14[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_28 (Conv1D)              (None, None, 32)     1056        multiply_7[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "add_7 (Add)                     (None, None, 32)     0           conv1d_25[0][0]                  \n",
      "                                                                 conv1d_28[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_29 (Conv1D)              (None, None, 32)     1056        add_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_30 (Conv1D)              (None, None, 32)     2080        conv1d_29[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_31 (Conv1D)              (None, None, 32)     2080        conv1d_29[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, None, 32)     0           conv1d_30[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, None, 32)     0           conv1d_31[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "multiply_8 (Multiply)           (None, None, 32)     0           activation_15[0][0]              \n",
      "                                                                 activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_32 (Conv1D)              (None, None, 32)     1056        multiply_8[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "add_8 (Add)                     (None, None, 32)     0           conv1d_29[0][0]                  \n",
      "                                                                 conv1d_32[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_33 (Conv1D)              (None, None, 32)     1056        add_8[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_34 (Conv1D)              (None, None, 32)     2080        conv1d_33[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_35 (Conv1D)              (None, None, 32)     2080        conv1d_33[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, None, 32)     0           conv1d_34[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, None, 32)     0           conv1d_35[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "multiply_9 (Multiply)           (None, None, 32)     0           activation_17[0][0]              \n",
      "                                                                 activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_36 (Conv1D)              (None, None, 32)     1056        multiply_9[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "add_9 (Add)                     (None, None, 32)     0           conv1d_33[0][0]                  \n",
      "                                                                 conv1d_36[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_37 (Conv1D)              (None, None, 32)     1056        add_9[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_38 (Conv1D)              (None, None, 32)     2080        conv1d_37[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_39 (Conv1D)              (None, None, 32)     2080        conv1d_37[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, None, 32)     0           conv1d_38[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, None, 32)     0           conv1d_39[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "multiply_10 (Multiply)          (None, None, 32)     0           activation_19[0][0]              \n",
      "                                                                 activation_20[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_40 (Conv1D)              (None, None, 32)     1056        multiply_10[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "add_10 (Add)                    (None, None, 32)     0           conv1d_37[0][0]                  \n",
      "                                                                 conv1d_40[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_41 (Conv1D)              (None, None, 32)     1056        add_10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_42 (Conv1D)              (None, None, 32)     2080        conv1d_41[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_43 (Conv1D)              (None, None, 32)     2080        conv1d_41[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, None, 32)     0           conv1d_42[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, None, 32)     0           conv1d_43[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "multiply_11 (Multiply)          (None, None, 32)     0           activation_21[0][0]              \n",
      "                                                                 activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_44 (Conv1D)              (None, None, 32)     1056        multiply_11[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "add_11 (Add)                    (None, None, 32)     0           conv1d_41[0][0]                  \n",
      "                                                                 conv1d_44[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_45 (Conv1D)              (None, None, 32)     1056        add_11[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_46 (Conv1D)              (None, None, 32)     2080        conv1d_45[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_47 (Conv1D)              (None, None, 32)     2080        conv1d_45[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, None, 32)     0           conv1d_46[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, None, 32)     0           conv1d_47[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "multiply_12 (Multiply)          (None, None, 32)     0           activation_23[0][0]              \n",
      "                                                                 activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_48 (Conv1D)              (None, None, 32)     1056        multiply_12[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "add_12 (Add)                    (None, None, 32)     0           conv1d_45[0][0]                  \n",
      "                                                                 conv1d_48[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_49 (Conv1D)              (None, None, 32)     1056        add_12[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_50 (Conv1D)              (None, None, 32)     2080        conv1d_49[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_51 (Conv1D)              (None, None, 32)     2080        conv1d_49[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, None, 32)     0           conv1d_50[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, None, 32)     0           conv1d_51[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "multiply_13 (Multiply)          (None, None, 32)     0           activation_25[0][0]              \n",
      "                                                                 activation_26[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_52 (Conv1D)              (None, None, 32)     1056        multiply_13[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "add_13 (Add)                    (None, None, 32)     0           conv1d_49[0][0]                  \n",
      "                                                                 conv1d_52[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_53 (Conv1D)              (None, None, 32)     1056        add_13[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_54 (Conv1D)              (None, None, 32)     2080        conv1d_53[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_55 (Conv1D)              (None, None, 32)     2080        conv1d_53[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, None, 32)     0           conv1d_54[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, None, 32)     0           conv1d_55[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "multiply_14 (Multiply)          (None, None, 32)     0           activation_27[0][0]              \n",
      "                                                                 activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_56 (Conv1D)              (None, None, 32)     1056        multiply_14[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "add_14 (Add)                    (None, None, 32)     0           conv1d_53[0][0]                  \n",
      "                                                                 conv1d_56[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_57 (Conv1D)              (None, None, 32)     1056        add_14[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_58 (Conv1D)              (None, None, 32)     2080        conv1d_57[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_59 (Conv1D)              (None, None, 32)     2080        conv1d_57[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, None, 32)     0           conv1d_58[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, None, 32)     0           conv1d_59[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "multiply_15 (Multiply)          (None, None, 32)     0           activation_29[0][0]              \n",
      "                                                                 activation_30[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_60 (Conv1D)              (None, None, 32)     1056        multiply_15[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "add_15 (Add)                    (None, None, 32)     0           conv1d_57[0][0]                  \n",
      "                                                                 conv1d_60[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_61 (Conv1D)              (None, None, 32)     1056        add_15[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_62 (Conv1D)              (None, None, 32)     2080        conv1d_61[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_63 (Conv1D)              (None, None, 32)     2080        conv1d_61[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, None, 32)     0           conv1d_62[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, None, 32)     0           conv1d_63[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "multiply_16 (Multiply)          (None, None, 32)     0           activation_31[0][0]              \n",
      "                                                                 activation_32[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_64 (Conv1D)              (None, None, 32)     1056        multiply_16[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "add_17 (Add)                    (None, None, 32)     0           conv1d_4[0][0]                   \n",
      "                                                                 conv1d_8[0][0]                   \n",
      "                                                                 conv1d_12[0][0]                  \n",
      "                                                                 conv1d_16[0][0]                  \n",
      "                                                                 conv1d_20[0][0]                  \n",
      "                                                                 conv1d_24[0][0]                  \n",
      "                                                                 conv1d_28[0][0]                  \n",
      "                                                                 conv1d_32[0][0]                  \n",
      "                                                                 conv1d_36[0][0]                  \n",
      "                                                                 conv1d_40[0][0]                  \n",
      "                                                                 conv1d_44[0][0]                  \n",
      "                                                                 conv1d_48[0][0]                  \n",
      "                                                                 conv1d_52[0][0]                  \n",
      "                                                                 conv1d_56[0][0]                  \n",
      "                                                                 conv1d_60[0][0]                  \n",
      "                                                                 conv1d_64[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, None, 32)     0           add_17[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_65 (Conv1D)              (None, None, 128)    4224        activation_33[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, None, 128)    0           conv1d_65[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, None, 128)    0           activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_66 (Conv1D)              (None, None, 1)      129         dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "lambda_1 (Lambda)               (None, None, 1)      0           conv1d_66[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 104,385\n",
      "Trainable params: 104,385\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With our training architecture defined, we're ready to train the model! We'll leverage the transformer utility functions we defined earlier, and train using mean absolute error loss.\n",
    "\n",
    "For this expansion of the full-fledged model, once again we end up more than doubling the total number of trainable parameters and incur the cost of slower training time. These additional parameters are due to the increase in filters for the pre/postprocessing layers. Training a model at this scale will take quite a while if you're not running fancy hardware - I'd recommend using a GPU. When constructing this notebook, I used an AWS EC2 instance with a GPU (p2.xlarge) and the Amazon Deep Learning AMI, and training took about an hour. \n",
    "\n",
    "This time around, we'll go ahead and use all of the series in the dataset for training, and train for 15 epochs to give this more complex model more time to try to reach its full potential. \n",
    "\n",
    "This is only a starting point, and I would encourage you to play around with this pipeline to see if you can get even better results! You could try selecting/engineering different exogenous features, adjusting the model architecture/hyperparameters, tuning the learning rate and number of epochs, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "145063/145063 [==============================] - 250s 2ms/step - loss: 0.3701\n",
      "Epoch 2/15\n",
      "145063/145063 [==============================] - 239s 2ms/step - loss: 0.2860\n",
      "Epoch 3/15\n",
      "145063/145063 [==============================] - 240s 2ms/step - loss: 0.2790\n",
      "Epoch 4/15\n",
      "145063/145063 [==============================] - 240s 2ms/step - loss: 0.2752\n",
      "Epoch 5/15\n",
      "145063/145063 [==============================] - 240s 2ms/step - loss: 0.2729\n",
      "Epoch 6/15\n",
      "145063/145063 [==============================] - 240s 2ms/step - loss: 0.2712\n",
      "Epoch 7/15\n",
      "145063/145063 [==============================] - 240s 2ms/step - loss: 0.2700\n",
      "Epoch 8/15\n",
      "145063/145063 [==============================] - 240s 2ms/step - loss: 0.2692\n",
      "Epoch 9/15\n",
      "145063/145063 [==============================] - 240s 2ms/step - loss: 0.2684\n",
      "Epoch 10/15\n",
      "145063/145063 [==============================] - 240s 2ms/step - loss: 0.2677\n",
      "Epoch 11/15\n",
      "145063/145063 [==============================] - 240s 2ms/step - loss: 0.2672\n",
      "Epoch 12/15\n",
      "145063/145063 [==============================] - 240s 2ms/step - loss: 0.2666\n",
      "Epoch 13/15\n",
      "145063/145063 [==============================] - 240s 2ms/step - loss: 0.2661\n",
      "Epoch 14/15\n",
      "145063/145063 [==============================] - 240s 2ms/step - loss: 0.2657\n",
      "Epoch 15/15\n",
      "145063/145063 [==============================] - 240s 2ms/step - loss: 0.2653\n"
     ]
    }
   ],
   "source": [
    "first_n_samples = df.shape[0]\n",
    "batch_size = 2**10 \n",
    "epochs = 15\n",
    "\n",
    "encoder_input_data, decoder_target_data = \\\n",
    "    get_data_encode_decode(series_array, exog_array, first_n_samples, date_to_index, \n",
    "                           train_enc_start, train_enc_end, train_pred_start, train_pred_end)\n",
    "\n",
    "model.compile(Adam(), loss='mean_absolute_error')\n",
    "history = model.fit(encoder_input_data, decoder_target_data,\n",
    "                    batch_size=batch_size,\n",
    "                    epochs=epochs)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Building the Model - Inference Loop\n",
    "\n",
    "Like in the previous notebook, we'll generate predictions by running our model from section 3 in a loop, using each iteration to extract the prediction for the time step one beyond our current history then append it to our history sequence. In each iteration we'll also update the exogenous features to include values corresponding to the next time step we'll predict. With 60 iterations, this lets us generate predictions for the full interval we've chosen. \n",
    "\n",
    "Recall that we designed our model to output predictions for 60 time steps at once in order to use teacher forcing for training. So if we start from a history sequence and want to predict the first future time step, we can run the model on the history sequence and take the last time step of the output, which corresponds to one time step beyond the history sequence. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def predict_sequence(input_tensor):\n",
    "\n",
    "    history_tensor = input_tensor[:,:(-pred_steps+1),:]\n",
    "    pred_sequence = np.zeros((1,pred_steps,1)) # initialize output (pred_steps time steps)  \n",
    "    \n",
    "    for i in range(pred_steps):\n",
    "        \n",
    "        # record next time step prediction (last time step of model output) \n",
    "        last_step_pred = model.predict(history_tensor)[0,-1,0]\n",
    "        pred_sequence[0,i,0] = last_step_pred\n",
    "        \n",
    "        # add the next time step prediction along with corresponding exogenous features\n",
    "        # to the history tensor\n",
    "        last_step_exog = input_tensor[:,[(-pred_steps+1)+i],1:]\n",
    "        last_step_tensor = np.concatenate([last_step_pred.reshape((1,1,1)), \n",
    "                                           last_step_exog], axis=-1)\n",
    "        history_tensor = np.concatenate([history_tensor, last_step_tensor], axis=1)\n",
    "\n",
    "    return pred_sequence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Generating and Plotting Predictions \n",
    "\n",
    "Now we have everything we need to generate predictions for encoder (history) /target series pairs that we didn't train on (note again we're using \"encoder\"/\"decoder\" terminology to stay consistent with notebook 1 -- here it's more like history/target). We'll pull out our set of validation encoder/target series (recall that these are shifted forward in time). Then using a plotting utility function which is updated to handle the addition of exogenous features in the input data, we can look at the tail end of the encoder series, the true target series, and the predicted target series. This gives us a feel for how our predictions are doing.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "encoder_input_data, decoder_target_data = \\\n",
    "    get_data_encode_decode(series_array, exog_array, df.shape[0], date_to_index, \n",
    "                           val_enc_start, val_enc_end, val_pred_start, val_pred_end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def predict_and_plot(encoder_input_data, decoder_target_data, sample_ind, enc_tail_len=50):\n",
    "\n",
    "    encode_tensor = encoder_input_data[[sample_ind],:,:] \n",
    "    pred_series = predict_sequence(encode_tensor)\n",
    "    \n",
    "    encode_series = encode_tensor[:,:(-pred_steps+1),0].reshape(-1,1)\n",
    "    pred_series = pred_series.reshape(-1,1)   \n",
    "    target_series = decoder_target_data[sample_ind,:,:1].reshape(-1,1) \n",
    "    \n",
    "    encode_series_tail = np.concatenate([encode_series[-enc_tail_len:],target_series[:1]])\n",
    "    x_encode = encode_series_tail.shape[0]\n",
    "    \n",
    "    plt.figure(figsize=(10,6))   \n",
    "    \n",
    "    plt.plot(range(1,x_encode+1),encode_series_tail)\n",
    "    plt.plot(range(x_encode,x_encode+pred_steps),target_series,color='orange')\n",
    "    plt.plot(range(x_encode,x_encode+pred_steps),pred_series,color='teal',linestyle='--')\n",
    "    \n",
    "    plt.title('Encoder Series Tail of Length %d, Target Series, and Predictions' % enc_tail_len)\n",
    "    plt.legend(['Encoding Series','Target Series','Predictions'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generating some plots as below, we can see that our predictions are often strong and expressive, with similar performance to those generated by the full-fledged model without exogenous features. This suggests that the exogenous features we've added may not contribute additional predictive signal and reinforces the effectiveness of a purely endogenous model. It's possible that we haven't fully tapped into the exogenous features' potential and likely that other feature engineering techniques could help as well, but for now this would argue in favor of choosing a simpler, more efficient model over this significantly more complex one. Set out on your own to try to prove this conclusion wrong!  \n",
    "\n",
    "One feature engineering trick that seems particularly promising is to hard-code certain long term seasonalities (i.e. quarterly or yearly) as additional features in the input sequences. I'd call this type of feature a **lagged endogenous feature** since it's derived from the actual time series. You can check out [Arthur Suilin's model description here](https://www.kaggle.com/c/web-traffic-time-series-forecasting/discussion/43795) for his breakdown of this idea. I may explore this technique in a future notebook, so stay tuned!  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict_and_plot(encoder_input_data, decoder_target_data, \n",
    "                 sample_ind=16534, enc_tail_len=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict_and_plot(encoder_input_data, decoder_target_data, \n",
    "                 sample_ind=16555, enc_tail_len=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict_and_plot(encoder_input_data, decoder_target_data, \n",
    "                 sample_ind=4000, enc_tail_len=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict_and_plot(encoder_input_data, decoder_target_data, \n",
    "                 sample_ind=68000, enc_tail_len=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict_and_plot(encoder_input_data, decoder_target_data, \n",
    "                 sample_ind=6007, enc_tail_len=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2WboUSVftxDGv82RMWuJJjPevO7HkZH/Mhdn+Z0f2TgCGYN73Gqvt4Rn26TiM+bkWI0iexhixsdLcszGp4zql1K5Z9NXmOquvF2I8N0GMPxBtZu0eh5kxWoMRCM7JApdjXqvVmPIWv3f4sMZaff66B326CSNmajETFV7samcrOnQsxjhdi5nB+w/aX6eFmO/TjZjv0HKMJ9LmT8DZSqlaZWbEJVOHSX0vw3xH/g+4TFszc6226jFpyjpM9G5CJgO1Ip2HYETASsx14c9AobXL0cC3SqlGzISd46wbpFJMujHdZ/0C4JfWd+tW2sV7JtyKqSWoMdeabI7tEuszfQzGOrEI87l6lHYrQTbnvhczmeE5zPvyOO1p+z9gbiDrlJmR7uxDDPO6TsO83nOA87TWmfjvAphr1SZMBM2NsVcIWwhXPC7lTwRB6DuUUs8Dc7XWN/Z1X7pCmZUfRmutr+2j87+KMVzf1hfn39wopQ4DfqS1Pqev+yIIWxIRYoIgbFGUmS5fhYmW/AiTLtxNWzWtBINS6nsYz9BaTHmQxzDeRpnVJghbEeIREwRhSzMGk2YrwwiN00SEpWQCRqSWYmbAzRQRJghbHxIREwRBEARB6CPErC8IgiAIgtBHiBATBEEQBEHoIwakR6y6unGz5FPLygLU1rZ0v+NWiox/8I5/MI8dZPwyfhn/YB3/lhp7ZWVx2oLcEhFz4PV6ut9pK0bGP3jHP5jHDjJ+Gb+Mf7DSH8YuQkwQBEEQBKGPECEmCIIgCILQR4gQEwRBEARB6CNEiAmCIAiCIPQRIsQEQRAEQRD6CBFigiAIgiAIfYQIMUEQBEEQhD5iQBZ0FQRBEAQhd+y7755ss812iccHHngwM2fO2izneumlF1m48Gsuvvg3PPfcM/h8fg499LBet/vee+9w771/IxaLE41GOOaY4znqqJ9kfPzChV/zyiv/5sILL+l1X7JBhJggCIIgDHJ8Ph8PPvjYFj/vUUcdk5N2IpEIs2dfzz33PMSwYcMJh8OsX782q+MnTZrMpEmTc9KfbBAhJgiCIAhCSo455nAOPfQw3nvvbSKRCH/4w82MHz+BlpYWbr/9jyxc+DUul4tTTz2TGTMO5PXXX2HOnAeIx+Pstdf3Offc8wH4979fYM6cBykuLmK77XYgLy8PgPvuu5uCggAnnjiT8877OZMnT+HTT+fT2NjEZZf9jl122Y1QKMT111/DsmVLGDt2PBs3VvOrX/2mg2hqaWkmGo1SWloKQH5+PuPGTQCgtraWP/3pBqqqqgA4//yL2XnnXbnvvrvZtKmKZcuWM2zYCI488mieeOIRZs++nWAwyG23zWbZsiVEIhFOO+3n7LPPDJYuXcKNN15LW1uEeDzGddfNZuzYcb16jUWICYIgCEI/4ak3FvPRwg05bXPapGH89IDtutyntbWVWbNOTDyeOXMWBx54MAClpaXcf/+jPPvs0zz++Bx++9vf8eCD91JYWMTDDz8JQENDAxs3VvO3v/2Z++57hOLiYi6++DzefvstJk+ewn333c199z1CUVER559/Fttvr1L2IxqNcs89D/PBB+9y//33cMcdf+XZZ5+muLiYRx55mqVLF3PqqT/rdFxJSSnf//6+/OQnh7P77tPYe+99OOigQ3C73dxxx5/46U9/xi677Mr69ev51a/O49FHnwFgyZIl3Hnn3fh8fj75ZH6ivYcfvp/dd5/G5ZdfTWNjI2eeeQp77DGd55//B8ceewIHH3wobW1txGLR7N6MFIgQS0FNQ4iN9SF2GDukr7siCIIgCJudrlKT++13AABK7cj//vcmAPPnz+Paa29I7FNSUsI777zFbrvtTllZGQAHH/xDPv/8E4AOzx9wwMGsWrUizbn2T5zLTi0uWPAZxx57AgDbbLMd226bWlT+9re/Y8mSxcyf/yGPPz6Hjz76kCuuuIb58+exfPmyxH7Nzc20tLRYfTkAn8/fqa158+by7rv/4/HHHwEgHG6lqmo9O+20Mw8/fD8bNlSx334H9DoaBiLEUvLCe8t45/N1/P6M6YweWtjX3REEQRAGCT89YLtuo1dbmry8fAA8HjfRaGSznis/35zL7fYQjWYfbdp2WyPUDjnkRxx77BFcccU1xOMx7r77AXw+X6f9CwoKUrYTj8e5/vrZifSmzYQJE9lppym8//67XHLJBVxyyeXsvvu0rPvpJCflK5RSP1RKaaXUYqXUb1Nsv00p9Zn171ulVJ1jW9Sx7YVc9Ke3TN2mgjjw3/mr+rorgiAIgtDvmDZtOs8++3TicUNDAzvuOIXPPvuEuro6otEor7/+Grvu+h0mTzbP19fXEYlEePPN/2R1rqlTd+GNN14HYNmypSxZsrjTPi0tLR1Si4sWaUaMGGH19bv84x9PdtjWHdOn78UzzzxJPB4H4NtvFwKwZs1qRo0azbHHHs/3v78fS5Ysymosqeh1REwp5QHuAn4ArAY+Ukq9oLX+2t5Ha32RY/9fArs5mghqrXftbT9yyW7bV1JR4uf9L9dz9H7bUlSQ19ddEgRBEITNRrJHbPr0vTjnnF+m3f+UU07n1ltvZubMn+J2ezjttDPZb78DOPvs8zj//LMSZv199pkBwGmn/ZyzzjrNMuun9oel48c/Ppbrr7+ak046lnHjJjBx4rYUFhYl7RXnscce5o9/vAGfz09BgZ8rrrgGgAsvvIRbb72ZU045nmg0yi677MYll1ze5TlnzTqdO+64hVNOOZ5YLM6oUaOYPft23njjP7z66kt4vV7Kyys4+eRTsxpLKly22uspSqm9gGu01odYjy8D0FrfmGb/94GrtdavW4+btNbJr2iXVFc39q7TaaisLKa6uhGAVz5cyVNvLubYGdty6HfHb47T9Tuc4x+MDObxD+axg4xfxi/j78/jj0ajRCIRfD4fa9as5sILz+Wxx/6RmHnZG7bU2Csri13ptuXCIzYacObwVgPTU+2olBoPTATecDztV0rNByLATVrr53LQp16zzy4jee7dpfz3k9UcvOdYPG5ZhEAQBEEQtjStrSF++cuziUQiQJyLL/5NTkRYf2FLm/WPB57RWjsdeOO11muUUtsAbyilFmitl3TVSFlZAK/Xs1k6WFlZbP4HDpw2jpffX86S9c3svcuozXK+/oY9/sHKYB7/YB47yPhl/DL+/ksxL7yw+WI0fT32XAixNcBYx+Mx1nOpOB74hfMJrfUa6/+lSqm3MP6xLoVYbW1LT/vaJckhyu/vNJyX31/OP974lh1G9ecPaW7o7+Hpzc1gHv9gHjvI+GX8Mv7BOv4tmJpMuy0X+baPgO2VUhOVUvkYsdVp9qNSahJQBnzgeK5MKeWz/h4K7A18nXxsXzGyopApE8tZtLqeNdVNfd0dQRAEQRC2MnotxLTWEeA84FXgG+AprfVXSqnfK6WOcOx6PPCE1tpptN8RmK+U+hx4E+MR6zdCDGC3HSoBWFE1OO8WBEEQBEHYfOTEI6a1fgl4Kem5q5IeX5PiuPeBqbnow+ZiRHkAgPU1mycdKgiCIAjC4EUq63dDQohtEiEmCIIgbH3U1tYya9bJANTUbMLtdjNkiFmO6J57HtosMxS1XkhtbQ3f/e73Om1raWnh5pv/wLJlS4nH4xQXl3DrrX/B7++8FFE6brjhWk466ZROlfH7IyLEumFIUT6+fI9ExARBEIStkrKyssQ6k/fddzcFBQFOPHFmxsdHo1E8nuwqGXz77UKWLl2SUog99dRjDB8+gmuvNeVIV6xYjtebuVyJRqNcfvnVWfWnLxEh1g0ul4sRZQHWbmomFo/jdqWtySYIgiAIWxWXXnoRGzdWEw6HOe64Ezn88KOIRCIcdthBHHro4XzyyUdccsnl1NXVctddd1BQEGDq1J3ZsKGKG2+8hZaWFm67bTbLly8lEolw+ulnM23anjzwwD2Ew618+unHnHLKaey//0GJc27cuJFx49oLqY8fPyHx98sv/4tnn32KtrYIU6bszMUXX0osFuvUn7vuuoOLL76U7bdXfPDBezz44L20tYUZM2Ycl112FQUFBdx11x3Mm/c+4GL69O9x7rnnb8FXth0RYhkwoiLAiqpGaupDDB2SeoHQTGgKtvHUG4v5yX7bUFrUefFRQRAEYXBT+O2V+KpyWzOrdfhRNO9wXY+OvfLKaygpKSUUCnHGGTPZb78DCAQCNDU1seuuu3HBBb8iFApxwglH87e/3cfw4SP43e/al5x+8MF7mD59L6644hoaGhr4+c9n8dBDj3PqqWeydOkSLrjgV53OedhhR/LrX/+SN954nd13n8ahhx7GmDFjWbp0MW+//SZ/+9v9eL1ebr75ev7zn9c44ICDOvTHSW1tDY8++hB33PE3/H4/Dz10H08//TiHHXYkH3zwHq+88hIbNzbR2Nh3E/JEiGWA07DfGyH26bfVvLtgHWOGFXHwtLHdHyAIgiAIfciTTz7Gu+++DcCGDRtYu3Y12223A3l5eey77/4ALF++lHHjxjNixEgAfvCDQ3jllX8DMG/eh8yd+z6PPPIQAOFwK1VV67s856RJO/LUU88zb95c5s+fxxlnzOTvf3+I+fPn8c03X3PGGcbP1toaYvjw4QAd+uNkwYIvWL58KWeffRoAkUgbU6fuSklJKW63iyuvvJLddpvO3nvv09uXqseIEMsAW4itq2lhyjYVPW6nORQBoK6xNSf9EgRBELYumne4rsfRq1zz0Ucf8vnnn/L3vz+Az+fnnHNOp7U1DIDP58OVgVUnHo9z4423MHr0mA7Pf/75p10eFwgUMmPGgcyYcSDxeJy5c98nHo/zox8dwZlnntNhX3sdylT9icfjTJ++F7/73R86bbv33jl8++0XPPfcizz33DPcdttd3Y5ncyALKGZArkpYNAXbAKhtEiEmCIIg9G+am5soLi7B5/OzdOkSFi5MXeZzwoRtWLlyBVVV64nH4/z3v68ntk2f/l2eeebJxONvv10IQCAQoKWlOWV7n3/+WSJVGA6HWbFiOSNGjGSPPabzxhuvU1dXB0B9fR3r13cdXZs6dWc+/fQT1qxZDUAwGGTVqpW0tDTT3NzM/vvvz/nnX8yiRTrDVyX3SEQsA3JVwqI5ZAkxiYgJgiAI/Zy99vo+L7zwT0466VjGjh3P5MlTUu7n9/u56KJLufDCcykoCDBp0o6EwyZyduqpP+fOO2/h5JOPIxaLM2bMGG666Va+851pPPbYHE499UROPrmjWX/16pXccouZMRmLxdl7733YZ5/9cLlcnHrqz7nwwnOJx2N4PF4uueQyhg4dmnYM5eUVXHbZ77j66stpazO/wWed9Qt8Ph9XXHEJsViUtrYI5513Ua5etqxxxePx7vfqZ1RXN26WTne15tSv7noPgFt+sXeP2//rPxcwX1czbEgBN529V4/b2VwM5vXGYHCPfzCPHWT8Mn4Zf2/H39LSQiAQIB6PM3v2DWy77bYcc8zxOerh5mMLrjWZNo8rEbEMGVEe4JsVtbSGo/jys6uXYmN7xGqbWonH4xnl1wVBEAShv/Pcc8/w2muv0NYWZtKkHTn88B/3dZcGDCLEMsQWYlW1LYwbnn4V9a5otjxibZEYLa0RCv25r1YsCIIgCFuaE088mRNPPLmvuzEgEbN+huTCsN9kecRAfGKCIAiCIIgQy5gRFb037DcHI4m/pYSFIAiCIAgixDKktxGxtkiM1rZo4rFExARBEARBECGWIRUlfrweN+t6KMRarLRkwGdseVJLTBAEQRAEEWIZ4na7GF5ewPqaFnpS8qPJmjE5prIQkNSkIAiC0H/Yd989mTXrRGbO/ClXXvkbQqFQj9v65JP5XHrphQC8++7/mDPnwbT7NjY28uyzTyceb9xYzZVXXtrjcw9ERIhlwYiyAK3hKHVN4ayPtWdMjh5WBEhqUhAEQeg/+Hw+HnzwMebMeYq8vDyee+6ZDtvj8TixWCzrdr///f2YOXNW2u1NTY3885/tQmzo0Equu2521ucZyEj5iixIGPZrWigr9mV1rC3EKksLyM9zS2pSEARB6JfsssuuLF68mHXr1nLxxecxefIUtF7In/50BytXruC+++6mrS3MqFFjuPzyqwkEAsyd+z533nkLfr+oXodAAAAgAElEQVSfnXfeNdHWSy+9yMKFX3Pxxb+hpmYTf/zjjaxduwaAX//6tzzzzBOsWbOGWbNOZNq06Rx99LFceumFzJnzFK2trdxyy00sXPg1Ho+HX/7yYr7znT146aUXeffdtwmFQqxdu5p9953BuedeQDQa5aab/sDChV/jcrn40Y+O4LjjftZXL2PGiBDLAtuwv7q6iR3Hl2V1rF26orDAS1mRT1KTgiAIQkp2n3NvyufP3XUPTp9qRM65/3mZD9et6Xzs8JH8/eAfATDn6y+4/eN5fDzzjIzPHYlEmDv3faZP/x4Aq1ev4oorrmXKlKnU1dXx0EP3cfvtf6WgoIBHHnmQJ598lBNPPJnZs6/njjv+xpgxY7nqqstStn377X9it92+w403/oloNEowGOTss3/J0qVLePDBxwBYt25tYn87Zfnww0+yYsVyLrroFzz++LMALFr0LQ888Ch5eXmceOJP+MlPjqO2tpbq6g3MmfMUQGK9yv6OpCazYMfxZbhdLt75fF3WPjG7dEWRP4+yYh8NLW1EotmHeQVBEAQh17S2tjJr1omcccbJDB8+gsMOOxKAESNGMmXKVAC++moBy5cv5ZxzTmfWrBN55ZV/s379OlauXM7IkaMYO3YcLpeLQw45NOU5PvnkI4466hgAPB4PRUVFXfbpiy8+45BD/h8A48dPYMSIkaxatRKAPfaYRlFRET6fjwkTtmH9+vWMGjWatWvXcNtts5k7930KCwtz8tpsbiQilgXlJX52V5V8tHADemUdk7KIijUnImJ5DLHSmnVNrQwtLdgsfRUEQRAGJplEsP56UGqx42Tm5J2ZOXnnjM5pe8SS8fv9ib/j8Th77DGda6+9ocM+ixbpjM6RS/Ly2lem8XjcRKMRSkpKePDBx5k37wOef/4fvPHG61x++dVbvG/ZIhGxLPnBHmMBeH3+qqyOs9eZLCzIY0iRJcQaszf9C4IgCEJfsNNOU1mw4HNWrza/f8FgkJUrVzBu3ATWrVvLmjWrAXj99VdTHr/77tMSkwCi0ShNTU0EAgFaWlKXhdpll1157bWXAVi5cgVVVesZN2582v7V1dURj8eYMeNAzjzzHL79dssLxJ4gEbEs2XZ0CRNGFPPZoo1sqAsybEhmEa0my6xf5DceMZBaYoIgCMLAoaysjCuuuIZrrrmCtjYTSDjzzHMYN248l156BZdccoFl1t+NYLCzuLrggl8ze/b1/Otfz+N2e/j1r3/LlCk7M3XqLsyc+VO++929OfroYxP7//jHx3LLLTdx8snH4fF4uOKKa8jPz0/bv+rqDdx447XEYsY6dNZZv8jxK7B5cPWkJlZfU13duFk6XVlZTHV19+a+D75czz3/+pqDp43l+AO3z6jtPz7+Kd+sqOXuX8/g88Ub+etzX3LCgdvzg2lje9vtnJHp+LdWBvP4B/PYQcYv45fxD9bxb6mxV1YWu9Jtk9RkD5i24zBKC/N554u1BFsj3R+A8Yj58jzked0Jj5hExARBEARhcCNCrAd4PW72/85ogq1R3v9yfUbHNAcjFBWYTHBZwiMmQkwQBEEQBjMixHrIfruMAuDzJRsz2r851Eah38zyKC3Kx4VU1xcEQRCEwY4IsR5SWuSjvMTHqqqmbveNRGOEwlEKC4wQ83rcFBfmS2pSEARBEAY5IsR6wbhhxdQ3h6nvRlAlSlf42yep2tX1B+JkCUEQBEEQcoMIsV4wbripCrxyQ9dRMXudyaKC9gJ0Q4ryCUditGRo9hcEQRAEYetDhFgvGDusGICVVV1PfXVW1bexFw0Xn5ggCIIgDF5EiPWC8VZEbFW3ETE7NemIiBXLzElBEARBGOyIEOsFFaV+Aj4vK7ox7NtV9ZM9YiARMUEQBEEYzIgQ6wUul4txw4vYUNNCKJze69VlalJmTgqCIAjCoEWEWC8ZO6yYOLC6ujntPrYQc5r1y0vMivZVNcHN2j9BEARBEPovOVn0Wyn1Q+AOwAPcq7W+KWn7LOCPwBrrqb9ore+1tp0CXGk9f53W+qFc9GlLkZg5WdXIdqNLU+7T7hFrf7lHVAQoCeTx9fIaYvE4blfaZagEQRAEQdhK6XVETCnlAe4CDgUmAycopSan2PVJrfWu1j9bhJUDVwPTgT2Bq5VSZb3t05Zk3HB75mR6n1iq1KTb5WKniRXUN4dZ3Y3ZXxAEQRCErZNcpCb3BBZrrZdqrcPAE8CRGR57CPC61rpGa10LvA78MAd92mKMrAjg9bhYtSF9CYvmFGZ9gKnblgOwYOmmzddBQRAEQRD6LblITY4GVjker8ZEuJL5iVJqX+Bb4CKt9ao0x47u7oRlZQG8Xk/Pe9wFlZXFWR8zfmQJq9Y3Ul5eiMfTWduGIjF8+R5GjRzS4fl9d8/nnhe/ZuGqemYdkf15Nwc9Gf/WxGAe/2AeO8j4Zfwy/sFKX489Jx6xDHgReFxr3aqUOgt4CDigp43V1rbkrGNOKiuLqa7uujhrKkaWB1iyup4FuorRlUWdttc3tlLo96Zse+LIEr5ZVsOKVbUE/Fvq7UhNT8e/tTCYxz+Yxw4yfhm/jH+wjn9Ljb0rsZeL1OQaYKzj8RjaTfkAaK03aa3tOg33ArtneuxAYNywrpc6ag61dSjm6mTKxHJi8TjfrKjZbP0TBEEQBKF/kgsh9hGwvVJqolIqHzgeeMG5g1JqpOPhEcA31t+vAgcrpcosk/7B1nMDinbDfmdVHYnGCLZGO/nDbKZuWwGIT0wQBEEQBiO9zoVprSNKqfMwAsoD3K+1/kop9Xtgvtb6BeB8pdQRQASoAWZZx9Yopf6AEXMAv9daD7jQ0Fg7IpZi5qS9qLdzxqSTiSNKKCrIY8HSGuLxOC4pYyEIgiAIg4acmJK01i8BLyU9d5Xj78uAy9Icez9wfy760VcU+LyUFfuorutcnLV9xmRqIeZ2u9hpYjkffl3Fmo3NjEnhMRMEQRAEYetEKuvniJJAPg0t4U7P28Vci9JExMD4xAC+XDrggoGCIAiCIPQCEWI5oqQwn3BbrNOak02JYq7pg49TtjE+sa+WiU9MEARBEAYTIsRyREmhiXg1NHeMinWXmgQoLcyntCifqlpZd1IQBEEQBhMixHJESWE+AA3NbR2ebw7Z60ymF2IA5cU+ahtbicXjm6eDgiAIgiD0O0SI5YjSgBFi9UkRsSYrIlbURWoSoLzYTzQWp7Glrcv9BEEQBEHYehAhliOK7YhYkmHfTlUWW0ItHWUlPgBqGkKboXeCIAiCIPRHRIjliPbUZEchVttoFhQoK/Z1eXx5sb/D/oIgCIIgbP2IEMsRdmoylRAr8Hko8HWTmpSImCAIgiAMOkSI5Yj0EbEQQ4q6joZBe0SsRiJigiAIgjBoECGWI4oK8nC5oN7hEQu3RWkORSjvJi0J7alLSU0KgiAIwuBBhFiOcLtdFAfyO0TEapuMqBqSgRArLcrH5ZLUpCAIgiAMJkSI5ZCSZCHWYBv1/d0e6/W4KS3Mp6ZBImKCIAiCMFgQIZZDSgvzCIWjhNuiQHtErLsZkzblJX7qmqSoqyAIgiAMFkSI5ZBkw36mpStsyot9RGPxToZ/QRAEQRC2TkSI5ZCEELOq49upyUzM+tCewpT0pCAIgiAMDkSI5ZBOEbEszPrQXkustlEM+4IgCIIwGBAhlkNKAh2XOaptDOH1uCgu6HrBb5vyEomICYIgCMJgQoRYDikt7Ljwd01jK0OKfLhcroyOl1pigiAIgjC4ECGWQ5ypyUg0RkNTOGN/GLR7yWp6kJqMxeLEYpt3tuW6Tc08+vq3tEWim/U8giAIgjBYECGWQ5xCrKE5TJzM/WEAQ4p8uF2urFOTsVic3979AXNe01kdly2Pvv4t//14NQtX1m3W8wiCsJURbaFg+e24whv7uieC0O8QIZZDigN5uDBCzE4vlmdQzNXG7XZRWpTfwaz/1mdruPzvcwm2RtIe1xxqY2N9iE8XbSS+mWqQLV/fwNfLa4HO62kKgiB0Rf6mtyhadBX+9U/1dVcEod8hQiyHeNxuCgvyaGgJZ11DzKa8xEdtY5hYLE48HueVD1eyvqaFqtqWtMc0h4xIcwrAXPPS3JWJvxtaRIgJgpA5rmij+b9NoumCkIwIsRxTWmiWOarpqRAr9hOLx6lvDrOyqokNtUGgXWylojnYlvh72bqGHvS6a6pqWvhYb6DA5wGgsbmtmyMEQRDacUVNlN8VaezjnghC/0OEWI4pKcynORRhY50RUNkKsTKHYX/eN1WJ51u6EmKhdmG0dDMIsVfmrSQeh8P2mgC0zwoVBEHIiJi5HrqiTX3cEUHof4gQyzG2YX9Flbnzyz412V5L7KOFGxLPO8VWMs3BdpG2fF1u7zjrm1p5b8F6hg0pYMZuowFolNSkIAhZ4IpJREwQ0iFCLMfYRV1XbmjC5WoXZplil7D45NtqNtaHKAmYYrBdRcSaQh1Tk7lcNPy/n6wmEo3xw+njKPB58eV5xKwvCEJW2KlJdyT3EXtBGOiIEMsxJYVGOLWGo5QU5uP1ZPcSl1nLHH30jYmG7bPLKKC7iJjZVlbsIxSOsn5TemN/ttgRtumThwNmZqiY9QVByAaJiAlCekSI5Rg7IgaZL/btxC53EYvHKfB5mTZpGNCdR8xsm7pNOZBbw34wHMHjduHPN0b90sJ8GlvaNluZDEEQtkISHjERYoKQjAixHONMRZZlUUPMprQwH4/bLIn0nR2GJpZNysSsP2ViBZBjIdYapcDnTSzTVBzIJxqLdzmLUxAEwYnMmhSE9IgQyzEdhFhR9hExt9vFkCLTxp47Difg9wLQkoFZf9L4MjxuF8tyaNgPtkYS0TBoH58Y9gVByBRJTQpCekSI5ZhSpxAryV6IAUwcWULlED87ji8jz+shz+vuuo5YqA2vx0Wh38vYYUWs2tBIJBrr0bmTCYUjFPi8ice2B04M+4IgZExCiDWA2BoEoQPe7ncRsqE40LuIGMDPj9iJaCyeMPoH/N6uU5PBNgr9ebhcLiaOLGH5+kZWbWhi4siSHp3fJhaPE7JSkzb2+BpapKirIAiZ4YpaHjFixi/mCfRxjwSh/yARsRyT53UTsIRLtjXEbLweN7689nRgoT+v61mToQiFBSZSZYuvXPjEWsNR4kCBIzVZ6ljYXBAEIRPs1CRIelIQkhEhthmwfVQ9FWLJBPxeWlojKeuDxeJxmkNtFFpesokji4HcCDF7ofGUEbFeCrFN9SFmP/YJVTW5K7UhCEL/xCnEpJaYIHQkJ6lJpdQPgTsAD3Cv1vqmpO0XA2cAEaAaOE1rvcLaFgUWWLuu1FofkYs+9SXlJT6q64IMyZEQK/R5icch1BpNmPdtQq0R4nETNQMYWVGIL9/DolX1RGMxPO6ea+1UQswuMNtbs/6CZZtYuLKOeQs3cPj3JvSqLUEQ+jlRiYgJQjp6HRFTSnmAu4BDgcnACUqpyUm7fQrsobXeGXgGmO3YFtRa72r9G/AiDOCEg3bggmN27pBe7A0BS2S1tHZOTzZZ3rHCAiOW3G4Xu+9QyYa6IM+/u6xX5w2GowD4fZ1nTfbWI9ZkHb9+U3Ov2hEEof/jsuqIgdQSE4RkcpGa3BNYrLVeqrUOA08ARzp30Fq/qbW2c1BzgTE5OG+/ZfTQQqZsU5Gz9goTJSw6G/btqvp2RAzgxIN2oHKIn3+9v4Ivlmzq8XkTEbH89ohYYUEebper16nJRkuIrc3hKgAA8Xicvzy7gJfmrshpu4Ig9ByXRMQEIS25SE2OBlY5Hq8Gpnex/+nAy47HfqXUfEza8iat9XPdnbCsLIDXm5toUzKVlcWbpd3eMLS8EIA8f16n/q2qMXeaw4YWdth2+anTueTOd7jv399wx8UzqCwryOhczjby1hgvx7CKjm2XFuXT3Brp1WvVZvndqmpaqKgowm0Vse0t9U2tfPJtNV8s2cTh+22XWEQ9U/rj+7+lGMxjBxn/5h1/a+Kv0oI26Ievtbz/g3f8fT32LVq+Qil1ErAHsJ/j6fFa6zVKqW2AN5RSC7TWS7pqp7Z28xi8KyuLqa7uh3drMVMTbO36BkaWdhQWa9cbseSKxjr0vdTn4YSDtmfOq5obHviQ3570HdyursVO8virrL8jbZEOzxf689jUEOzVa7XReg9D4SiLlm3MWjClY9WGJgAi0RhPvPoNx87YLuNj++37vwUYzGMHGf/mHn9FJJhIvzTWbiDUz15ref8H7/i31Ni7Enu5SE2uAcY6Ho+xnuuAUuog4ArgCK114vZIa73G+n8p8BawWw76tFVhpyZTFXW1y1rY5SuczNh1FLttP5TFa+pZtjb7mUrBVuMRc5r1wRR1DbZGaYtEs27TptHhMVubQ59YfVP7nfdbn67psv6aIAhbBruOGIBbPGKC0IFcCLGPgO2VUhOVUvnA8cALzh2UUrsBd2NE2AbH82VKKZ/191Bgb+DrHPRpqyKQpUfMxuVy8b0pIwD4cllN1ucNhTt7xMBh2G/uuWG/ySHE1uXQJ1ZveddGVgQItkZ567NO9wSCIGxJ4lFc8TBxt5lF7oo09XGHBKF/0WshprWOAOcBrwLfAE9prb9SSv1eKWXPgvwjUAQ8rZT6TCllC7UdgflKqc+BNzEeMRFiSdgiK1VRVztKllzWwmbH8WW4XS6+XJa9ab8lRfkKgJJEdf2eG/abgm3kec3HL5dCrM6KiB3+vQkU+Dy89tGqXkXuBEHoJTHznYzlVwLWMkeCICTIiUdMa/0S8FLSc1c5/j4ozXHvA1Nz0YetmURErDW71KQ5No9tRpWwZG09LaG2RCmMTAglUpMdJ0aU9LK6frgtSmtblB3GlLJodX1OS1jUN9kRsUJm7Dqalz9cyXtfrmfGrqNzdg5BEDLHTkvG8ivxhFZL+QpBSEIq6w8A7IhY6tSkea4oTUQMYMrEcuJx+Hp5bVbnDVqpSX9SRKzYKura04hYk5VOLS/xU1Hqz2kJizpLHJYW5fODaWPxely8Nm9VN0cJ2fLfj1dz61OfEYvJAs5C19hV9dsjYiLEBMGJCLEBgL12ZerUZBsuV2ex5GSnieVA9j6xVHXEwJGa7GFEzDbqFwXyGFlRSENzuMu1NLOhoakVl8v0cUiRj6nbVLC+poWN9cHuDxYy5rNF1Xy5tKbXKywIWz92RCwuQkwQUiJCbACQn+fG43aljoiFIhT687osTTFxZAkBn5evltUQT7FeZTqCrVG8HnfCy2VjpyYbe1hdvzFofryLC/IYWREAcucTq2sOUxzIT9QlmzSuDICFK+py0r5gsFddSJUuF4QOWB6xuKeQuKdQhJggJCFCbADgcrko9HtTl68Iti/4nQ6328XkCWVsagixPotFtoOtkU7+MOi9Wb8pERHLZ9RQU6x23cbc+MTqm8IMsYQiwKTxlhBbmV1aVugaO1oqQkzoDnt5o7i7gJinWMz6gpCECLEBQsCfR0tS+i4ej9Mcaktr1HdiL7n0VRbpyWA40mnGJJg6YtCL1KTlESsuyGNEuRURy0IgpiPYGqG1LUppUfti66MrCykqyGPhytqsooGpeOuzNfz5H18QtQrsDgQaWsK0hnM/azRktRmUOm1CN9jLG8XdPuLeYqkjJghJiBAbIBT6vbSEIh3ERLgtRiQaT1lDLJmdJmTvEwu1Rjv5wwDyvB4KfJ4e1xGzU5rFgbycRsTqHUZ9G7fLhRo3hJqGVqrreucTe3/Bej5dtJHVGwbGQuXRWIyr7v2Qh15dmPO2JSImZIwdEfMUEPcWSx0xQUhChNgAIeDPIxqLE25rj8a0l67ovgpJRamfkRUBFq6spS3SfUQnGovR2hZNmZoEKA7k93rWZFFBXuJfLiJidlX9UkdqEhw+sZW984ltsJZlWrpuYKRWWsNRGlraWLSqPqftxuLxRERMhJjQHS7LI4bbb4RYLAix3EzOEYStARFiA4T2ZY7aL2BNXVTVT8VOE8sJt8VYvKb7H2b7hzZVahKMYb+xJUysB+m+JkvAFVtes1EVAarrgr0uvGpHxIY4UpPg8Imt6LlPLNgaocGK5C3N4PXrD7Raon1TQyin6UlnW5KaFLojMWvSU0DcU2w9J+lJQbARITZAKEixzJFt3u/OrG+jxhpBsnh195EhO/XkT5GaBGPYj8fbl1jKBjs1aUfyRlQUEo9DVU3vUod1VjHX5IjYqIoAJYX5fNMLn5gzrTlQImJhh7DNZpJGdwQdUTCJiAndYdcRsz1iICUsBMGJCLEBQqqIWGKdyQzM+gDbjS4BYEkGC4AH01TVtykJ9Nyw32TN9PS4zcdvlFXCYuHKWl6au4Kr75/Hs28vybpdOzWZHBFzuVxMGjeE+qZwjwXJhtp2IbZuU0vO6p5tTpxp7FwurB50RMREiAndYgkx3AUixAQhBSLEBggBX+fq+rYYKMowNVla5GNoqZ8la+q7jQwF06wzaZNY5qgHtcQag20UBdqjViMtw/5j/1nEM28tYdWGJt78ZE3Wac9UZn2b3vrE7IjY6ErT12UDICoWbmsXTLlczzPkEF9BEWJCNyRmTXoKiHnNzaAIMUFoR4TYAKE9IpYiNZmBWd9mu9GlNIci3UaGuhNitr8r28rqsXicppY2ih1RvIkjSxhSlM/44cWcdPAO7KEqaQ5FWL0hu9lV6cz60Huf2AZLiH138nAAlmYQVexrOgqxXEbEHKlJ8YgJ3dBeR8yX8Ii5pZaYICQQITZACCTWm0yRmsxiIe9tR5cCsGRN1xdC+8c2nRAbYkWdvlyabbX+CLF4nCKHECsqyOPW877P1adO44DvjGHnbYcCoFdlF72qaw5T4POSn9c5nTq8rIAhRfnoHvrE7NTk9B0HjhBrdaQmcxsRc5j1JSImdEfCI+ZITYpZXxASiBAbIKSOiGXnEQMTEQO6nTlp/9gW5Kf2iE2ZWMGYykLeXbCOV7NYVNtZQywdatwQAL7NMo1Y3xROCMRkjE+sjIaWNqpqs58UsKE2yJCifIYOKWBoqZ+laxt6XSB2c+M061fVtOSsEK2Y9YVssFOTePziEROEFIgQGyAE7FmTjh++5mB2syYBxgwrJD/PzZK1XQuxxKzJNBExX76HC4/dhbJiH0+9uZh531RldP4mx4Lf6Rha6qe8xIdeVZex2IlEYzQF21KmJW0mjDT+lOXrs4tmRaIxahpDDCszkwq2GVVCU7Atka7sr7RaqUmvx0U0Fu8w4aA3OM36EhETusO5xFE84RGToq6CYCNCbICQEGLO1KT1dyALIeZxu5k4ooS11c1d+nvs1GQgjRADKC/xc+Gxu1Dg83Dvv75mUQZlMdoX/E4vmFwuF2rsEJqCbazNsOJ+Q8Ko70u7z4QR5m58+brs7sY31oeIx2HYkAIAthlloor9PT1pz5ocXVkE5C492SEiJh4xoRs6lK/wmM+iK9q/vzuCsCURITZAsH1gztRkU9CsBWmXgciU7caUEqfrmX92+Qp/mtSkzdhhRZx71FQi0TivfLiy23NnkpoEUNYsx0x9YulqiDkZN7wIF7BifXZCzK6oX1lmCzFzV9/vhZiVmrQFaK4M+7YQy89zEwpHicX6d4pW6GOsgq54pHyFIKRChNgAwZ/vwe1ydSpfkU1a0mbbUd37xLqbNelkp4nllBX7MvJNOZc36go11vjEdIY+sXQ1xJz4872MqAiwoqoxq9IYdkrPjoiNH16Ex+1iaTfp3b7GjojZQmztxtxExEJWtLSixA90nEUpCMnYSxzF3X5iIsQEoRMixAYILpeLgN/bsaBrqC0ro77NNnZh1xwJMTAlKOqbw9Q2tna5X1MiIpY+cgUwrKyA0qL8jH1idV3UEHMyfkQxoXA0K7+U7QUbZkXE8rwexg0vYmVVU6+XZdqc2B6x0UOL8HrcOYyImXbLbSEm6UmhC1wdFv021x63CDFBSCBCbAAR8HsTEbG2SJRwW4yiHkTESgL5DCsrYMnahrSRofYljrpOTdpMHGnudLsrdGrXHevKrA/tPrGG5syq4SciYl2kJgEmjMjesF9d21GIgfGJRWNxVlb1X9OxXUfM7/MworyAdTUtOZnpaUfAyotN9FFmTgpdEg0RxwWufMdak/07rS8IWxIRYgOIQr834RFrL+aafUQMTBmLYGskrYE7GI6S73Xj9WT2EdnGmpHY3TqMjVZqsjiDfifSkxn4xOyq+iVdpCahZ4b9DXVBCv3eDvXa7Ar7uVjDsSnYxsOv6sQYcoWdmszP8zCyopDWcLTbiGUmhFqjuF2uxMQImTkpdIUrFgS3H1wucPuIu/IkNSkIDkSIDSAC/jwi0RhtkWgixZdNMVcn7YVdU6cnQ62RjNOSAONHlOAClnVjYG8KtuH1uDKKtO1gGfYzqSdWb5n109URs8nWsB+Lx6muC1E5pKDD88OtUha5EGKfLdrIW5+u4X+frel1W05ss77P62aktZ5nLtacDIYjFPg8iRm1MnNS6ApXrJW4x289cBH3FosQEwQHIsQGELYxvykY4Z/vLAVglLVOY7aMtUoaVKUREsHWSNoaYqkI+I0Rfvn6ro3wjS1higrycLlc3bY5qiJAcSCPz5ds5LV5K7tcaLuuqRWvx91luQ3I3rBf19hKJBrrkJYEU6kf6FFx2GTsVN/i1bk1/zsjYvbnZF0ODPuh1gj+fG/K2naCkIwrGiTubv/+xL0luKL9N6UvCFsaEWIDCFtk/PPtpXy6aCOTxg1hxm6jetRWSaGJpDWkSYcFw1ECvsz8YTYTR5YQCkdZ30W9qqZgG0Vd1BBz4nK5OGLvibRF4jzxxmJ+9Zf3mPOaTlkuob7ZVNXPROBlY9ivSuEPAxhS7CM/z82GHETE7EW0l6ytz2kpCNusn+d1M7LCEmK5iIi1RjtGxESICV0RC4G73TIQ90hETBCciBAbQNjrTb67YB2lRfmcdeSUrGuI2ZRYpvb6FIt2m/RnDH9+dmMvIUoAACAASURBVBMBJlo+sXSG/Ug0RrA12m0NMScH7j6GW37xPY7df1sKC/J485M1rK7ueDcdi8dpaA53WUPMyYThlk8sA8N+tTVjMjk16Xa5GDYkQFVtsNcGeLtSfbA1ypoMC9hmQjgSxeN24fW4GVFegMvV+6Ku8XicYNhESwusiJjMmhS6whUNEve0f39i3mJckQbo50uECcKWQoTYAMJOTbpdLs45ckrGwiMV/nwvvjwPDU2dhVi2pStsEoVO0wixTIu5JlMcyOfQ6eP5wR5jAdjUEOqwvSnYRjQW77KqvhN7qaNMfGLJNcScDC8voLUtmigm21NCjojS4gxWJ8iU1nAssQB6ntfD0FI/63oZwQu3xYjHoSDfKxExISNcsVbibn/icdxbhIs4RHN30yEIAxkRYgOI4eXGcP3T/bdlB2tGYW8oKcxLGRGzIzQFWaYmx1QW4fW40hr2My3mmo7yEiO0aho6zvyzjfrd1RCzsQ37mcycbK8hFui0zTbs25X3e0rIsXbjom4WY8+GcCSKL6/9Kz60tICG5nCirEVPsP1skpoUMiIexxULdhRiVgkLqSUmCAYRYgOI3bYfyi2/2JuD9xyXk/ZKCvNpbG7rZFq3U00FWaYm87xuxg4rYtWGJtoisU7bmyzR110x13TYBUSTI2L2Y7uuVXdkY9jfUNtCvtedUuQNL8+NYd+5ZFAuDfvhtmgiIgbtlfB7U8Kivb6cIzUpQkxIh1VVH48zImYt/B0VISYIIEJsQOFyuSjLUGxkQkkgn1g8TnOw42xEewmbbGZN2kwcWUI0FmfVhs6zohp7GRGzhURNkhDbmMbH1RWZGPajsRjrN7UwvDyAO8UkgFyVsLAjkJPGlbGxPkRdU+9rfYFJI+Z724WYHVFMFrLZEHJES6V8hdAdiar6HWZN2sscSVFXQQARYoMa22OWPHPSXsKmu1IQqejKsN9Tj5hNaWE+Hrerk5DYWG8eDy3NXIjZ/Vy4sjbtPus2tRCOxBhvFYFNxk4VpysBkimmHISH7cdYa4DmKCqWnJqsKE0dUcyGhH8w34vX4ybf65aImJAW5zqTNrLwtyB0RITYIKYkrRCzI2LZecTAYdhP4ROzlzfKpKp+KtxuExFM9ojZMxuHDvGnOiwlu243FIBPdHXafWwz/4Q0QqwkkIc/35PVupWpCIWj+PM9bGcV2V2UAyEWjcWIROMpU5PJr1822CLdjpYW+LziERPSE7W+Gx4RYoKQDhFig5h0JSxsQ3ZPImLDywP48j2s3ND5ImsLvuJezPYsL/EniqzaVNeF8OV5shJ4lUMKGDe8iG9W1NKSplCsbeZPFxFzuVwMLzclLDIpDpsOU6ney8SRJXjcLhav6f3MyUQxV68jIpbGY5cNdtq6wFoZIeD3SkRMSIsrZj5rzohYzCMeMUFwIkJsENOemuwoRJyG7Gxxu1xUlvo7+bgAaiyTeHlx5pGrZCpKfMQh4aOKx+NsrA9SOcSfUTFXJ7urYURjcT5bvDHl9uVVDbhdrsQqBKkYXlZAJBpLOd5MCbZG8ed7yc/zMH5EMSurmhLFWHuKPTPSGRGz/YXJfa1pCPH35xZkdM7k0iYFPrMQfS4WExe2PlzRrjxiIsQEAUSIDWrSpyZ7Vr7CprzET7A12snEXdMQwp/vSSyN09O2TVtGiDWHIoTC0az8YTZ7qEoAPk6RnozGYqyqamLU0MIOYiYZ27Df05mTkWiMSDSWeK23G11KNBZneTeLp3dHqzVr1efoe36eh5JAHpuSUpNvfbaGF99ZyhdLNnXbrj2xwE5bB3xeorF4ylmygmDPmoynSE1K+QpBMPT8F9GBUuqHwB2AB7hXa31T0nYf8DCwO7AJOE5rvdzadhlwOhAFztdav5qLPgndk0hNNnf8YW6vFdWzj0e5Y3ZjwN8eTappaE1s6ynJ6bWe+MNsRlYUMmpoIV8uq0mk3Gxso346f5iNXcJiQ00LO00oz7oP9ixEO/q43ehSXvtoFXplHcpa9LwntEfEOt5rlZf4WbOxmXg8noggrrXWn8ykrEWotWNpkwJHLbGuBGsuePKNRQwvCzBjt9Gb9TxC7rBnTdKhjpi5JkhqUhAMvY6IKaU8wF3AocBk4ASl1OSk3U4HarXW2wG3ATdbx04Gjgd2An4I/NVqT9gClAS6Tk1mW0fMpsIuvNrYngILtkZoaY0kSij0lPKkEhaJJYh6EBED2H2HStoiMRYsrenwvG3UT+cPs7FnTq6v6VlErP21Nh/7SePL8OV7+M/HqxMFcHuCc8FvJxUlftoisUQpEYC11rJKdRkIsfaImPlsBLZQLbHGljCvzlvF8+8ukzToAMIVtT1iHRf9BklNCoJNLlKTewKLtdZLtdZh4AngyKR9jgQesv5+BjhQKeWynn9Ca92qtV4GLLbaE7YA/nwP+V53p9RkqLepyWI7atX+w54Lfxg4a2GZ9hKlK3oQEQPYPZGe3NDh+eXdzJi0aU9N9qyERfsMVSNoigryOHLviTQF23jmrSU9ahMcETFv54gYtAvZSDSWmPVZm0H9slCScCzYQrXE7Lp09c3hXtdtyzX/+2wNf/7HFzldsH1rIVFHLFVB17bcLeclCAOZXKQmRwOrHI9XA9PT7aO1jiil6oEK6/m5Scd2m3coKwvg9W6ewFllZdc/vFsbQ0r8NIXaEuOurCwmYkUcxowuw+POzgAPsM0484MeisQS7a7aZC7IY0eW9Oo1LrSEXFMoQmXl/2fvvMPkuuur/7l3et/e1ctIlptsWXLBNtgYDNjGBkwzLUBIgXSSQAjhDSEJ4clLEl5SCDimx0DABuNecZFt2ZYtW7K06rsrba/Tyy3vH7dMb7uzsojueR492t2585t775R75nzP93wDxHTSuHFNx6LW7ejw09Pu5ZWjM2SysrnG6EwCURTYuqW3wGdVjE60XLSZSGpRjz8Z1UhwW4vHvP/73rKZZ/ZP8MTLo9xw5TrCqxoveR6fiuvregv2a2VfCBhBQqCzM8DQeMTs+IynpZrHYNj5V/S34HU76GzzAeB0O5f1vTO7b8L8eXQuxbmbepr+GIvd/z1HZnnp0DR2t4P2RSqzpwOW5fmLap8fgVArAWN9SZsZ6xbjuE+jz9sz7bO/GGfy8b/Wx94Uj9ipxtwSZ/tVQmdngKmpM0su97vtDI1HmZyM0NUVZGoqSiSaxuW0MTtTmo5fD0RFK4udGI+Y5/PYCS041SUKSz7HHpedsekYU1NRRsY1U7tNkRe97vnrO7jv2WF2D06yrtuPrCgcOTFPX7uPyHzt11pni4eh8SjjEwvYxMZE5vFJbf9VWSnY//ddtZ5/+OGL/L8fvcTnP7wNsUFCPDWjEbFsRipY16Xv3rGRedb3BNh3KNeoMDmbqHkOF3RlMxpJEo+mUGWNmo1NRphqXz4Ssv9orpHg+VfHuXB9e1PXX8p7f04vwR8bnkPp+fWM8liuzz73/BwBIBJXSRvrqyodggMpMc38afJ5eyZ+9ufjTD7+U3Xs1cheM0qTJ4EVeb8P6H8ru004HLYDITTTfj33tbCMCHqdyIpKPK+0lEhLZulpMWgNuBAoDA41SmFL9YiB5kEz1pueTxLwOhYVtWFgx+ZuAL5/734kWanbqG+gu9WLrKhmmbQRmB2qRec7vLKVS7b0MDQR5dEXG39LVCpNFqfrG/4w0DxitfxXxhQAY+ST5xQN/h6ZjOJ0iAS9DgaH504rn5gxmzWazNTY8sxDrjSZR9IFAdXRYpUmLVjQ0Qwi9hywIRwOrwmHw0408/0virb5BfBh/ed3AY8MDg6q+t/fGw6HXeFweA2wAdjVhH2yUCeKIyyyksxsJE1HA3Mbi2G3iQT9zoLgUIOUtS+xaxJy8RixZJbphdSioivysaonwOvP72NoPMovdx6v26hvwBz+vQjDfrLKXM93X7Ueh13kiZdHG17XIGKuIoJX7BEziNiaviAZSalJqIzwWQNG6G9yGT1iWUkjxys6/Wxc2cp8LMPk/NKmGTQTxjmLxhffXPG/FmVGHAEo9hZEi4hZsAA0gYgNDg5KwKeA+4H9wI8HBwf3hcPhL4bD4Rv0zW4F2sPh8GHgj4HP6PfdB/wYeBW4D/jk4ODg0pIsLTSE4nmTo9MJFFVlRVflENN60B50MxdNm/4jg5Q1Y2i5QeaOji4gKyqdizTq5+PmN6ynI+Tm7qeHeHrfOFDbqG+gx+icnInX2LIURmOEu4wCGfI56W33Mj6baDi5P20m6xeuG/A6sNtEs9lhbCaOy2ljox6VUatzUgufza15KhSx0ek4sqK9JjetbAFgcLjxi/jR0Qgzi1Atq0FVVbNRIZKwFLFiGIGuFBEx1dGCIM3DaaRsWrDwWqEpHrHBwcF7gHuK/vZXeT+ngJsr3Pdvgb9txn5YaBymIqZfRIzRRNXS5OtBW8DF0dEIkXiGFr+L2Wgav8fRlKwpo7xpXIyXqoiBRih+913n8cVbn+XV43Naon6dZHRlt0bYjE7LRpAbGVT+rdjb7mN4IsZcJG2WFetBRiqfIyYKAm16aVdWFMZnE6zo8psm87lYmv4qz30qI9HVmjvfRnxFPhEbm4nT3ept2NdWCUbH5IruABv1weiDw3NccV5f3WtkJZmv/HA3m1e18gc3n9eU/QItB84gycZQews5mCOObKWKmKBKoCTA5nstds2ChdMGVrL+GQ5DEVuIaUTMvOgtURHLT8BXVZW5SKop/jDIKWKDIzoRa4IiBnDRWT1cvEXzi/V1eOsmjV2tHrwuO0cXkYafm2JQnogZatvYbGNqW6UcMdDO30I8w9h0AklW6Wv3mSSvWqhrVtIGief72YpLky8fmeZz33yWZ14db2h/q8H4crCyy09fhw+/x8HgyHxDPrFYUiIjKYwvcUB7MfLz0yxFrBSmR0ws/LKkOjRl0ypPWrBgEbEzHsWK2InJGALQ37m0b6n5XqR4SrsILjVDrHhtYyj3YsNcy+F9V29goNPPjrO6676PKAis6QsyOZdsOITVUMTKlSYBett1IjbTWKdwuoJZH3KK4t5jWohtX0eOiM3HKpOJcn624tLk03rMxImpxsu0lWC8Jgc6/QiCQHhlC7ORNFMNlBnj+mD3uUiqqUb//Py0aNwiYiWQS0ccAah2Tdm0DPsWLFhE7IxHbsxRBlVVGZmM0dnqWVIXIuSl60dSpjG8GUZ9yBEJoyTUDI+YgYDXyRc/tp23XbK6ofut6dVCKo81qIoVJ9UXI+c/a4yImWb9CooYwN5jWiREb7vPLE1W84gVjzcCjUAKgkbEspLCHn2Aej0p/fVAVVWGJ2J0tXnNxoPwCsMnNlf3OnGdIGckpaBDeKnIL8lGlzAJ4X8ryo04AlAMRUyyiJgFCxYRO8ORG3OUYWZBU6+WWpaEnGo1E0mbRv1mlSZb/C709AQEgSXPr2wG1vbpRGy0MSJWnFRfjO42LwKa76oRZKTKpUnjfB3US7t9HV7zb9VKk0YZ1Z03cUEQBLwuO8m0xL7js+bszNkmEbHZSJpEuvA1uUlvLGjEsJ9PvmYjzTPsG0obUDKhwkKeR6zYrG/XnkNLEbNgwSJiZzw8Lht2mzbm6Liu5izVqA95pcloyoyuaG0SEbPbRFr82lptARd222v/Ml6rK2KN+sSSGQmbKOAoU0IETdFqD7kZa3CsT04RK13XUMQkWcVhF+kIecxuympjjio1FnhcdhIpid2DWjisQPMUsXx/mIG+Th8+t70xIpanVuXn2y0VBaVJSxErgdE1WZAjRs4jJliKmAULFhE70yEIAiGfg0giw7HRBWDpRn3IxSTMRlLm8O9mecQgRyaa0THZDAR9TjpCbo6ORhryIKUysl7eq9xh2NPuZSGWaWieoxnoWlYRyxHinjatu1EQBFoDTuarELFKjQVel514KsuLh6Zo8TtZ2R1gto5w2HpQrnlEFARW9QSYiaRML1wt5CtiM01UxPJLk+mMXPf+nDHQc8Qqliaz9ZeXLVj43wqLiFkg6HMRiWfMsloziJgoCLQFXMxG0szpCkSzSpP5azWrY7IZWNMbNENm64WWVF/dj9erz3NsZNh1WlIQBaHsrND8Um5fR64po9WvvQ5kfURVMXJm/UJy53XbyWQ179UFGztpC7qQ5OZ4sUYmNCJmRIQYaC8Kpq2F/BKi8cWgGTDIccivlfijVudkAQQliSo4QSi81Kh2QxFbeC12y4KF0woWEbNAyOdEklX2HZ3G47I1lFdVDW1BFwvxDBNzSQQwy4nNgHEhbmbH5FJh+MSONuATS6ZlPK7qMRm5zsn6fWKZjIzTIZZV2lwOG36PA4A+fW2AloALVc1FmRSjnFkfChWyC8NdZmhvM7xYw5NR/B4HLTrRMdChv0brJb35pHBuGUqTPa3aebSyxAohyKmSsiTkFDHLI2bBgkXELABBn3ZRno2kzYiAZsBQXoYnorQ02cvVrXcT9nacPmGQjXZOqqpKMlOHIqaTpUYVsWo5aAaRzVfEDKJcKcLC6PAsJo5Glpjf42DjipBJxKqVOetBMi0xNZ9iRVfpa9KcmVkvESvwiDWzNKmta7weLUWsCEqyxKgPufgKq2vSwlIQz2ZJy8s75/ZUoCnJ+hZ+vWFEWAAMNKEsacAgYrKi0taE0Ub5uPTsHnxuB1s3dDR13aVgVU8AURDqVsQykoKqlpb6itHTrpGlRrLEMlm5bIaYgfaQm6GJKL3teaVJ/Tmq1DlphJcWe8SM3y/Y2IFNFHOK2BIN+zv3aqGw6/pDpftvduU2Vpr0exxN6+iEPEVMJ2IRa95kAQQlDWUUMdVSxCw0Ad/Y8wJf3rWT/7nhXYzHY6wJtXBRT/0TN04XWETMghlhAc3xhxnI94S1Njliwm4TuTDc2dQ1lwqXw8ZAp4+hiSiSrNRUACuV+ooR9DrwuOyNlSazctVS8HWXrmJ9f8hU24CaSpYxF7N4fw116qLN3fo6ehTGEkqAqYzEXTuP43LaeOOFAyW3G00a9StiEk6HSFerh6HxKIqqIjZB+TWImDH2KZq0FLF8CHISxVn6ZUm1BVAFm5Wsb2FJGItrHlJVVfn0Yw+iqHDbtddzzeq1r/GeNQarNGmhQBFrJhHLD3BttiJ2umJNX5CspHCyjmT5SqW+YgiCQG+7l8m5JJJc3khfjEyN0uTqniDX7lhZUPIziFtFRayCWf8NW/v5/Ie3sWV1G5CnrC2hNPng8yeIxDO8+aIVBa9Pc18DTkRBaMAjlsXndtAWcCEratMyvxJpCY/LljPrN1ERk2SFp/eO1/2cn44QlFTJeCPtBgHVHrLiKywsCeM6ETu3s4vvXns9AhKffuxe4tlfL2XaImIWzHmTggADHU1UxPLI1+kQunoq0EieWG68UW1hurfNi6yodREPRVXJSkrZDLFqaKmhiFUqTTodNtMfB1r3JVQPh62GWDLLfc8O4fc4ePP2lWW3MUqgjZQmfW5HwQzUZiCRkvC67AS8haPCmoHnD0zyzV++apZof+2gqiAnwVb+va/YW6zSpIUlYSwew22z0eJy8ybvIT4deoKxRJqvv/jca71rDcEiYhZMxaG33WeOkWkG8slXexOjK05nGJ2Te4/O1NzWTKqv45z3NNA5ma0y8LsaWnVVp5ZHrNb+upw2vC77okNd73l6iGRa5rpLV1cchg5aSXQ+mq6pGMmKQjIt4/fYC2agNgOJdBav20HQqzW8NLNr0pilOTrdvLmdpxSqhIBS1qwPmk9MtOIrLCwBY7EYPT6tmccx8zCfaXuSXluUf33xOU5EGwvXfi1hETELtAXceF12zlnfXOO7x2U3L6RniiLW1+FjTW+QFw9NMzwRrbptqoLCVA6Gqb6emZPVBn5Xg8OuxVpUVMT0SAybWHvd1qBrUab42UiKh144QXvQxRu2VjfddoTcqNRuCjB8XEZpkjruUw8URSWZlvG67LgcNpx2samKmPE8TM4lm7bmckNMDuOYewrIzZmsRsQEJQVy87pYLZw5yMoy08kEvX6tiuOceRi/mOHLHQ+RkmW+vXfPa7yH9cMiYhZwOW387Scu5jdvPKfpaxtK2JniERMEgXdcoRlF73j8aNVtTc9VHYpYLkusNhGrlqpfCy1+VxWzvlSzscBAq99FMi2Z5dd6sefwNJKscO2OVTjs1fff7Jycr05UjAwxX5MVMeP587rtCIJAwOtsanyFoShOzDU23uq1hP/gXxJ64TrE1JhJsMrliIFWmgQrwsLC4qCg8k9veBO/ec4FiMlh7IlDSP5z+EDgZW7fsI/P7rjstd7FumERMQuA5hNzLeLCXQvnrGtnbV+QQBnD9f9WnLW6lY0rWthzZIYjJyuXXlKZ8iODyqGzxYNNFBibrV2mSlcZ+F0LLQEnybRcQqBUVWU+nsGvl+BqoVYURiWM6kTTKPFWg9GtOV2DVBkZYl63w+zkbYYiZihtRo5awOsgmsg2ZbQT5BSxqfkkitKcNZcbYnoUQZVxzP3KVMSKxxsZMNP1LZ+YhUXAZbPz/s1nc926DThnHgYg2f8h5NAFvFv4GXZplrQs8c2Xd5M4zc37FhGzsKy4+fXr+csPbWtKVMCvC/JVsZ9VUcVynqvaRMxuE+ls8TA2nah5oc8ssjQJlY3287EM6YxMb5u33N1K1zGM/w0SHsMDlx+rUQn1hroaGWI+t52gz4lNFJjLI2+vHp/lv+7ah9IggTKJmFsjp0Gfk6ykmAR7qTCCdSVZbepYpuWEkJ0FwDH7uJYhRvXSJFiDv88kxDLLE+9iELFs+9Wke96JoMq4Jn/B91/dy+eefIztP/gvfnH44LI8djNgETELFpYBG1e0cPaaNvYPzbF/qPxg41Sd8RUG+jt9JNJSzY4/g4gtRuGsRKDGdYLUUwdByl+nUeVpbCZBW9BVFzntqJeIJY3SpANREEq6LX/0yGHueOwwJ/QB4/UikTKUtpwiBs1J11cUtWDU1K+LT8wY4u2c/RWCrHvEKnVNWoO/zyi8MjXJ2d/+Bp974lFGmmCk/+H+vVz94+/zzMlhHLO/QvasRvauI919IwCu8Z9x88bN/PGFO4hl0nzq4XvJys35ktRsWETMgoVlwk26KvbLncfL3t6IIgawSh98XasJIGOWJht/e+ciLArJxJg+XskYQF4LixlzlExLzEXTBWn/1dAWqC9dP2ak6uvKVVvAxUIsgyQrjM3EGdEJ2FCN81qMRLq4NGkM/l56GSSayBSEzk78OhAxVUHQSZUtNYIt9qr293I5YlilyTMNU8k4rW4333zlRS75wW08MnxsSesdnp/llelJHIlBRGmBTPvVIAgo7gGyoR045p4kxDyf2XEZN23YREqWObJwepJ+i4hZsLBMWNMbZE1vkIMj82VN66YiVmdkyEqdiNUiDOnM4s36RmmyuBRmNAnUr4jppvgGFDHjMeopSwI47CIhv7NmtlquazLXwauikcTn9k+a2w2PN6qI5cz6kJtQ0YzOSSMMd3Wv9pxP/hoY9gUpgkAuSsQ5fT9QuTRpDv62Iiz+1+PzTz7GiWiUXbd8lK9d9WYEAT5+/93sm55a9JpjMe39ujKtZYZl2q82b0v33ISAgmvy5wBsbtMSAfbPTC/68ZYTFhGzYGEZcdbqVmRF5dCJ0ouNqYjVYdYHWNWttWkPT1QnDBlp8aVJY4j6SFGZzixNLqNHLOcPq3+Qe0fIzVw0XdXMbpj1fR5NEWs1DPuRNLsOTGK3iYiiwNBkY4pYvIiIBZqYJTYf1cjcxhUaWZmYPf0VMUMNy4YuAsA58whQuTRpKGJ1jTlSsrjGfgRK8+aEWjg1SEpZvvHybu46cgiHzcZ7N23h61dfSyyb4Za77zDT8RuFcb9VsYdRBRvZ1svN29JdNwHgmrwbgM3tFhGzYOGMxaaVrQBlfWKGIlZPfAVAyO8i5HMyXIMwZIxA10WY9TtDbnxuO8fHCx9jbDZBa8BVV4cnaOqTwy42pIiN6kSsr05FDLQIC1lRq5ZAc2Z9ozSpEYO9x2YYnY5z3rp2VnT5GZmINdSdWLk0uXRFzDieFZ1+vC47kzUiOk4HiCYR24Hs6jVjKSqb9bVh7vWY9d0nbyO49zdxj/2oSXtr4VTBmAdp5H0BvH19mL+8+HVsau/A59Dely9PTXBkfq7uruOxeIwOtxtv9AWk0Hbz9QSguHuR3SuxxQ8AcEF3Lw++6xb+8MIdzTqspsIiYhYsLCPWD4SwiQIHyhIxCYddrDkcPB8ruwPMRtLEkpVVl6XkiAmCwKqeAJNzSdOMns7IzEbSdathxjqtfldDiti4WZqsXxEzIyyqlCeLlSsjwuLR3ScBuGhzF2v7Q6SzckOZXcmSrknt/0gT5k0aRKwl4KKr1cPkXLLhrs5TDaNjUnW2kW27IndDBY+Y0oAi5pq6DwBbbP8S99JCLTw8dIysLGOLH27K+R7XS4i9vsLxeb+39SJ+8NYbCTi19+O77/opl/zwNv7+2adqrqmqKuPxGL1uFQGFTPtVJdvI3nXY0mMgxfA5HJzX1Y3XUV/8zqmGRcQsWFhGuBw21vWHGJqImsqMgWRartsfZmClXp6s5hNLL8GsD7CqR/ei6arY+Gxj/jADrQEXkXim7qHVozMJfG67WeKrBx3B2ob9eDKLTRRM5dFQxOIpCadD5Lx1Hawb0EhBI4b9eDoXiwE5j1gzFDEjPqTF76Sr1YMkK8w1aT7mcsFQxBR7K5nWK82/VyxN1htfISdwzD0BgC1xpAl7aqESHho6yvvuvoMP3HMHtl030fLCdaAubej8eEJTurt9hV+wBEEomNLxobPORRQEnh47WXNNSVF4x4ZNvK1bu7/sXlGyjezbAIA9cRjQZvAeX5gnLTcWMn0qYBExCxaWGZtWtqCqcHC48IKTzEh1+8MM1NM5uZT4CoA1PVqY6nH9MYwQ2XozxAy0BlyoUBDDUAmSrDA1l6S3w4fQ0nBFswAAIABJREFUQOZce0hTW6pFWMRSEj49/V67T44YnLeuA5dTI8tQ23+XD8Os73E1P77C6Fpt8bvobtXO++lu2BckjYipjtYCRaxiadIeQkWo2TXpzMsks4jY8uKSvhW8ceUaHh0Z5o1H38R0IoEtPrikNccqKGLF+IuLX8eaUAuDs9M1y5MOm42vvuFN/MVG/fNTLA0Ml7zrAbDpROyvdz7O9h/8Fy9PTZZs+1rDImIWLCwzNq/SfWLDheXJVFqu2x9mYGUdhv3crMnFEbESRWwRJUNoLF1/YjaBoqoNk726SpPJrGnUB03BMvxz2zd3AbBWJ2JD4/UrYom0hCDkPH4Ouw2300akGWb9WBqX04bHZaerVSObp3uEhZjRSpOKow3FsxLZs0a/ocKcWUFEtYdqjjhyTj8AgCp6sSWPgXL6KRqnHMvQtPDy1ARff/E5PrvjMj7UOcnz6X52jHycPceeWNK6Cirtbk9NIgawqa2d+XSaiUR9g+4FRfvCooqlI/Rkg4jFNSK2sbUNOD0N+xYRs2BhmbG2L4TDLhb4xBRFJZ2V657daKCjxYPHZauhiC2tNNlhGPbHdEWswVgJA0Ym2VwdWWJjiyR7tUqTqqqSSEmmUR+0kkhXqxePy845a9sBzefV1epheCJat1k4mZLwuuwFCl7Q62xKfMV8LG1GieQUsdObiOUrYgCZNq08qdorX4BVRwtCtkp8harinH4QxR4i3fkWBFVCTA03b6d/DWGff4aOhzp5YM+dyMrSyob52DU2yv99/hmOTR3lttB/8FedzzEktfCmJ6M8PXoCgEeGj/Phe3/Otu99i6FIfbEjv7f1IvZ/9Hc4v6un5rbhNu39eGB2pup2T5wY5s8ff5iXF/T3mlBqZ5B9hYqY2Tk5axExCxbOODjsIhsGQpyYipsX6UbmTOZDFARWdAUYn0mYeWHFWEp8BeQZ9uc1w/74bAKXw2YSq3phDHqfq2PAttkx2dEY2XM5bfg9joqlyVRGRlFV08dl4LffvoU/f//WgoaGld0B4impZkCsgXgqW0DwQCtPxpY4bzIrKUQTWVr8Wrmlq81QxE7v0qSYNRQxjYgl1v4Z8bWfJRuq3Kmm1FDEbPGD2FJDZNqvRvZv0v52hpcnZyZ380fT1/KBp47yvVdfadq6o3Hti9fq1JOIgsKnL34jD6y6g+sCw2zr7gXg23v3cO+xIwxHI/xqZKhpj23g6pVr+Isdl7EyWH3W7PPjY9y2dw+T+tQMtUxpUnGvQBWcJhELt3UgAAcsRcyChTMTRnlyUPeJGQGvjZYmQStPqsDIVPnyZE4RW/wQ99W6T+zYeJTx2QQ9bd6G54W2NDDmaLGKGGjlyZlIqiz5Kc4QM9DX4TMDcg3Um9NmIJGW8BQRvIDXiayoZrTFYrAQz3VMAgQ8Djwu2+mviBlmfYdWAlLcAyTWfRbEyl82VEcLghwHpXw51yhLZjquQfauA04dERPSk9jnnzklj9UIPrcvxr/MXwzAl555ksk6y3i1MKp7udbO/xRV9JLuvZnL+we4o/s2XNlxAP5s+6V89fXXADBYQ7UycNeRgzxThwEf4KKePv7wwh2sDbVW3c6MxHAailgpEUOwIXvXaq8XVcXncLAqGGJ/HR60Uw2LiFmwcApQnCfWaJhrPmoZ9pcy9NvAat0ntntwiqykNNwxCVpJzW4TeGbfOAs1ypNjM3GcdrHASF8vOoJuspJS1ptVHF1RDcXeuGqQZIVMVjEzxAzkIiwWX57MN+pDrpQ6OX96R1iI2TlUwQG2+sm0OeaogipmpPNn2k89EfMf/AtanrsWMXXilDxevTiZkBFQ+afuR4lk0nzhqV81Zd3xeAwBGJD2k+65CdUeJNtyCQAOnZCe3dHJjevDAAzO1SZiqqryuw/dyxeeeqwp+5i/rwD9Lu09r4rlO61l73pEaQEhoyX4b27vYDaVahp5bRYsImbBwinA6t4AbqeN/cdnUVW14fFG+VhZBxET0Eqii4VBSp47oHUYNWqiB/B7HLzr9euJJLJ865evViQRiqoyPrM41Q1yhKUc+SmeM1kNtc5rPorHJhkI+rR9KZ5M0AiM7DXDIwbQ3eohKykN5bKdagjZWc0f1sBzmBv8XUrEBCmCY34n2eBWVFeXScSMOILlhj36EgIK9oUXTsnj1YsTKYFeW5TfCzzO1vYWfnroAA8Plc5tfHjoGHceqr/jcTQWpduZxSEoJPs/DEC2RVPeHPNPm9v5nU4G/IG6FLG5dIq0LNNdh1HfwOeffIyrf/z9qqrVWDyG22ajVdTfD2VKk1AaYfHJ8y/iJ9e/k6CrMZvFcsMiYhYsnALYRJEta9qYmEuy58gMycziFbHedi92m8hQhRJaWlJwOmwNxUAUwzDsG8Gxi1HEAK7ZNsC569rZd3yO+58tb7KeXUiRWaTqBpjlwUSqjCJWoTRZDkGvk9aAq64sMTNVv4iIXXxWNzZR4CePHqno4auFuVhhaRJ4TTonxfQ43sNfBLk+z5yYnTPLkvUiN/i7NPDYMfMYgiqR6XiTvm0Qxdl5ahQxJW0+jiPy0vI/Xp1QVJXRjJ0Vjgg2QeWfz3VjF0U+dv8vCzoNb33lJd539x186uH7yMq1X4eqqqKoCivFSSTvBiTd1ycFz0cV3SUl2h9edxO/eu+Haq5bb3RFPsbjMV6ZnjRLpWXXjcfo8fkR0bsmy5Umyeuc1InY9t4+rlyxCo/99Ap2tYiYBQunCDdevhZRELj9oUPmPMLFeMTsNpGBTh8np2Jlw1IzWXnRHZMGBEEwy5OwOO+Wsc5H37aZkN/Jzx4/ypHR0k6rUd0f1rfIx/CZRKzUlxWvoFxVwqruAPOxDAs1SovmwG9XqffszdtXMhNJcdfO43U9ZjHMVH1/7uJidE6eSsO+e+Sb+I79I87pe2tvrCoI2XmzY7JemIpYmdKkY34nUDjMWfauR0wOg7L0ztRqsMUPI6gagbFHX1zWx2oEU4k4WVVkhV17H22zH+Db197A5y+5nG6vD1VV+erzz/DZJ7Q5nxlF5thC7ckFgiCw+51X8/TAt5BatudUTdFFNngh9ujegu7WTW0dtLnLT0zIx0RCI1M93vqJWK5zsrypXlFVOj1ebTvjdVBBESvOEjOQkk6vCJQlfVqHw+G2cDj8YDgcPqT/X/IuDIfD54fD4afD4fC+cDj8cjgcfk/ebd8Oh8PHwuHwS/q/85eyPxYsnM7o7/Bx9YUDTM4nuedpreOo0fgKA6t7AkiyypGTpcQmk5UXnSGWj1W6YV9AK40tFkGvk09cvwVFUfnRI6VlJXPYd8fiiJjh0ypnkDdUsnoUMcjltA0Olyo0BevqqfrFZn2A6y9bTXvQzf27hjk53bgXxRj43eIvVcR2vTpRdbxVM2HXx9vYEkdrbitICwgoZsdkvcgpYqWvY1tqFADZs9b8m+Rdh4CCLdn8jr182GOv5n6OvASL9Oa9OjPFs3Ua1evByZim1g440+a+vWn1Wj52jnbpfGbsJF/etZMVgSA/fNuN/Oo9H2JNqKWutW2pE4iCiuweKPh7tvUSvUT7nPk3VVWZSMSZqDGw21TE/IshYuVLn6Ig8Oh7Psj33npjXo5YpdJkYZaYqqpc9P1bue6O2+ven1OBpSpinwEeHhwc3AA8rP9ejATwocHBwS3AtcA/h8Ph/FfGnw4ODp6v/zt9NGALFpYBb3/dagJeh3mB9rgWR5i2bdKCSJ/aO15yWzqrLFkRg5xhvz3kXlIHJmhdo52tHqbKlNaMgdaLJXveaopY0lDE6iNi2zd3A/Dg8yNVt6vkEQMtNuT912xAVlR+8MBgwx1aOUUsR8TW9AbZtLKFA8PzfP7WZ9l7rL6OtaXAGJhcFxEr6pisF9UGf4vpMVTBhursMP+WM+w3ySemZGh55nXwUuGlyxbXSKji6EDMziKmqr8eykFWFH77wXu48c4f80/PPYlt8j5QJBRVNV8TaVnikeHjRDP1ef9Wh1r4cd+dvK8rguRZW0IS7zl2mHBrO7+86T28cdVaNrd34LDVfu8OLyzwyNBhJiUfiquQiEllfGK7J8c559vf4OsvPV91XaO7sdtb/5esTW3a811PM4CpiFUoTaqODhR7yHy9CIJA0Oni4OxMUzPYloqlflq/HfiO/vN3gBuLNxgcHDw4ODh4SP95FJgEOpf4uBYs/FrC63bwrivXmb+7F6mIbVrVSnvQzXMHJku8SBlJXjJxAq3BALRyWzMQ8mlhp4pSSEwisVIFqBEYQ7eLZ3lCzqzv89R3nvs6fJy/voMjJyMcOlG5pJMrTZZfd+uGTs5f38GB4XleGJyq67ENzMfS+D2OgmYLu03k0+/dyjuvXEsskeWrP9rDA881Tg7K4cHnRvjvB4pM3XLKJGC2ZKkRvBjGnMmGS5NVBn+L6XEUZw8IufPQSOeke+RWWp+6EEGKVNzGOfMIjujLcOx7BYTGHtNIaKr33drvi/CJ2USRf7jiarq8Pv7+uV1c9YtHuPq7f8ea//yaqWwlslne+8uf8cWn60uvb3Oo3Ox7ifNbRKTg+YjSfEHA7Z9vv5RH3/NBev3aezcry3Wl1N9z6BDvfHqeBxNrkT1FilhoOyqC2TkJsKFFI9y1DPuf3LqNZ275Dbb39td1fABrQi04RVvFvK/Dc7P8ePBVTkQjCKrRNVmeiCEI2vDvxFHQS82b2tpJyTJD0foCaU8FlkrEugcHB8f0n8eB7mobh8Ph7YATyH8X/a1esvyncDh8erUyWLCwDLjs3F5TbaqXIBRDFAQuO6eHdEbm+cHc7DRVVclkFVxL6Jg00BHy8Fs3bOHmN6xf8loAIb8LVYVoUWltPp5GFAT8DQz7zke10qRp1q9TEQO4dsdKAO6r0FyQ/1jVYjHecaVWUtt1oLHZdvOxdIE/zIAoCrztktX85Ye24XLY+NVLzSl53f3MELc/OGiOxgJNcRJQ9J9rEzEhmxtv1AgqDv5WVcT0GIqrMI29ISJ28jbsiUPY9BJrObgm7tB+SI4iJo+bf7fF96M4Ws1GgZ1Dr/DBe+5kKlGfRy8ry3zigbsZnJ3h0Xe/n7cHjrIn08PRpJOwc4pYRNv/gFO75L0wMVZtORO50Nx2pKBWjswniX6HE7s+SFtRVTbd9u/ccvcdNdc9EdHI6oA9glI0QFt1tCD7t+BYeN5s3Ai6XPT5/BysQcQ8dgdrQ634HPW//+yiyPs3n81Vq1aXvf2xkSE+9fB97J4Yz/OIVV5f9q5HUDOatxBYFdRU2BPR+seZLTdqXgXC4fBDQLnZBJ/L/2VwcFANh8MVNfhwONwLfA/48ODgoKEJfhaNwDmB/wT+HPhirX1qbfVib4IHphw6OwO1N/pfDOv4T83x/+XHLuaZvWNsO7tv0d2N11+5nl88dZxnD0xy41UbgdycSb/P1fCxlNv+uiubdz56dGXN5rQXPFYsKdEScNHdVT1NuxJk48KDUHIMGVn7SFo50IpNrH6ejft2dPgJP3GMlw5Pk1ZhoKvMOdAfs687VPE8d3T46W33se/YLC2tXhx1fGYl0xLJtExXm6/iup2dAVb0HGJoLEJ7ux+xxnFVQyotmbEf0bTCQJ/uGokfN7expUfpbHOArUrGW1y7QPtbe/E38rrTy2BeWxxv/v3SM6BmcARXFJ6H1vPgGfBkj+Op9jjJCYi+rN3FNQfltpXTMH0Pty2cz0rHAlfLL0LnuSAlIXEUui6nZc3r2LWzn7ce8gBHSTrkut5Xx+fnufPwID6Pkz91SdzZ810WLvoYQW8LwuDX4PD3Yc1TENrE9v5+XhwbI9TmxVmjjPihH+1k5/HfZ9dqaFtxCRyCkLwfOj9Qdvu1ra0cmp2lvcNfNRomn4i1DWwCe5EKvvJt8Oo/0LlwO4R/D4Cze7p54MgRnAEHIXf518bwwgJtHg9+ZwXFqgJue1dJcc3Egqq9Xjf1d+I6rFGJjq72yq/PzrNh/Me0O0ah81w292m2jggZ87l8ra97NYnY4ODgGyvdFg6HJ8LhcO/g4OCYTrTKfvULh8NB4G7gc4ODg6a+maempcPh8G3Ap+vZ6bll6hzq7AwwNXX6sORTDev4T93xi8Clm7uYnl583pQN2LSyhb1HZth3aJKuFg9RfYSSoKoNHcupOHanThiOjczh1z1sqqoyG0nR1+Fb9OOn9PLj7HyyZI35aAqvy87sTPXzXHz8V1/Qz+DwHP993wE+8pZNJdtPz2rlnkwqU3W/z1nbxgPPjfDk7hHOXtNe81gmZrXPNq/LVnXdNr+Tw5LCoWPTtAUbD8E1cCIv7+zF/eN0+DVlwTv2Ij5Adq/Clhpidngvsj9ccR33zCgBYCbpZu7EFCFXffskZBx0AOnYFJG847VFD9IGJOkgVnQe2lx9sHCQ2SrnxzX2CwxaH5s8TNJTuq1z6j6EVIqPTmoX/T/71Uv8ke9GXLFXaEUl6dzA7uNRbhj9ICIKt7757ax01Pc+eWVUu7R1ODwkB7+PB1A73850++vxKG34D32O+P7vklj3WcKhNnadPMnOg0Ns6aju2jkwNcdQNoRdkJlWNtABZMafZaEvClKM0J5byLbsILHuLwBYG2hhz8QELx49yUpdCSoHg4j1euxMzSlA4TEKnb9Fm+1f4ZW/YSb4LrD7WePT1tt5aIhtPX1l191223/iczjY9YGP1Txn9eLIlKYKurMimXQSJzA1kwKhfBOLixUEgdjYyyQdlxFUtdf4/rEppgaip+xzvxrZW2r94hfAh/WfPwz8vHiDcDjsBO4Avjs4OPg/Rbf16v8LaP6yvUvcHwsWzihcdo42A27nK9oH/1IHfi8nQj7tW/FCLBc9kEzLZCXFvG0x8LjsCFQuTS6m/HvBxk66Wj3s3Ft+KkA9pUmA89drxuOXDtU3324uWmrULweji3KpY4+MRgmAY2M5L5VdN+qnO98CgC1Z3bBvlMz+5OUYG279N/bXOc9Ptetm/SKPmJjWXs+Ku/QCL3vXaWn3cuVjd848nFtL774shmviDk5IORX2K8MtfOLBu02j/l3xDdx810+Zld18s+suru+vv5nkhO4B6/f5cE38HMXZSbb1dQCku68HcuVVg3ztna7tJTwZT9FvjyK42lAdbcjuVdgje0BVCe77XZyzj+I++R1z+416B+Khudnq+xuJ0GFL4PCWJ1Sqs4Pkqk8iZqbwDv87kOtuPFhh7awsM51M1N8xqap4j/4D9oXdHFuY5w8euZ8fD75aspmRqt/j8yMoGVTBBkJlJbE4S2xTWzuf2X4pVwysrG+/TgGW+mn9ZeCacDh8CHij/jvhcHhbOBz+lr7Nu4ErgI+Uian4QTgcfgV4BegAvrTE/bFg4YzCtnAXLqeNp14ZR1FVc+B3M8z6zUZI9z0ZsxTzfy7niaoXoiDgdtnLdk0mUlJD/jBzTVHgzRetQJIVHnup9EJey6xvYP1ACK/Lzp7D9c23MzomW2ucj84WnYjNL42ITVUgYrbYfhR7i9kxV8uwb3RNfueIRsB++8G7SUp1xGyIdhR70CRy5uOntW5g2dVbchfZuw4BtfI+qQrOmUdRRS17TUyXIWJKGufUPQyLmo/vD/tHucF3gHev7TeN+p94MctYPMaXwgofDb3InXsf55MP3VtXQOpJ3X+0kuOI2WnSXTeYMze1YdQOk4id3a6VyvbOVPcSSorCeEpmhX0B1aGRICl4PmJ2Gv/gp3FN3gmALT2GmNKI7MZWzbNXiSyBpkqfiETot0VQiqIr8pFc9XsojjY8Q19DyM7yhhWr+c5bbuCNq9aU3X4ykUCl/jBXe3QPviN/i2fk31FVlf8+sI/HygwWH4vH6PB4tDKukgGh+pcWyfAV6hEW3T4/f7ztYnY00ECw3FicU1jH4ODgDHB1mb8/D3xc//n7wPcr3P+qpTy+BQtnOlxOGxdt6uLJl8f4/X9+wlTCmpEj1myE9PE/+WGphjpmjAZaLLwuO8l04YU/k5XJSErdGWLFuOTsHn706GGe3jvODZetLvDyxVMSdptYk/DabSLnrmvnmVcnGJmMlQwaL0bxnMlK6GppriLWEnAxMZckkcridSrYEkeRWrYje7WLbK0IC1HSiNh716/i9sND7J+d4QtPPc5Xriy5PJRAcQ9oRmpVNYNETUXMVWpPzjfsy/6zSm63xfYiZiZJ9dyMa+Jn2MoQMefMo4jSAn29O/jzrku5zvMMl538KyKh67Gd1FSYz120jf6WXt7qPwovwpMnR/jJyXF+5/xtnF2jhGh0Ra5NPA5AuvuduRsFG7JntakyntPZyf3vfL+pMFXCRDyOAqywR8ymCCl4Hq7Jn+MZ+Sayq59013V4R76BPfIiGXcvG0wiVt1Uv/f9V5J49CZk9zsqbqPagyRW/wn+Q5/De/yf6d/wRfoDlV/P4wkjuqI+ImbTs9uE7AKrgiHcNlvZ/R6LxcxsNEHNVO6YNGD3I7t6mxd5sgw4/eoXFixYaAhv2bGSzataadVH4vjcdjYMVPaDvFYI6uXH/JmQ801QxEA75niRItZoqn4x3E47F2zsZHI+yZHRwgiERCpb1yBxgPM36OXJw7XLdfNlxhuVQ5eetL9kRUwncq87VytJHRuPYosfQkBB8m1G9mhETKzROSlkNMXla1dfy9Anfo/NbR18e98enjpZO2JD9qxFlKMI2dz5yRExTRGTFYVfjQxxcHaGrB7wWqlz0jmjp8p3vAnF1Vu2NGl0S65YcwN/su1iXrf5CgQBHHM7sccPoDg7+cj5l3HN6rVIwa0AXOjUuu5enpqoeUwhl4vVwRDror9EdvaQbb2k8Ji96xCzcwjZWTx2B1u7e/DW6Cw0yN0KxwKqTsSygfMAUEUXkfO+T6bjzQDYI7sBWBtq5StXXM1Hz66clS4IAqvt05zlmirpmCxGcsXHkV19eIb/A//+P8Jz/F9Qx+4qO+mg0TBXI0RXkCLYRJGBQJAT0dLokb0f+S1++DbdzK9kqnZMGpA9a7VytqK9vz7/1GNc+z8/bDjjb7lgETELFn7N0dvu40/ft5W/+fgOvvqp1/H//vAKM/D1dELA60Cg0CNmZIiFlqqIue2kMnJBSKORK7aY0qSBS7ZoiszTecG5JyZjTMwl6akzgPbsNe3YRKEun5ihFtbyzLX4nTjtIpNLbFyanE8S8Do4VyeLx0Yjpj9M9odRHS0ojraapUlRmtPm/YlePHYH33zT2/irSy7nkr7KpS4D5VQ3US9NGorYvceOcPNdP+V1t3+Hlb84xlUnPsxTx/eVXc/wh2Xar0Jx9WmlSTUvvFMvS8ruAaTQRdrf2rejCg6cMw9jSx5HylPaVGc7snsFFwkauamHiH3u4tfx0psHaFMnSHffWOJhKo7hUFSVYwvzxLKVRze1ezz8Qf80V3mOoTg19SzbehnpzrcSOftbSKELTdJoj2hjmZw2Gx85+zzO6az8eRDPZpmfO46qUrU0qe2wh1j470GV8Zy4lU898QJ9dwwiHfvPkk1NL5fXj5gcxjH7ZNWl7THt+RT13LeBQJDZVIp4tlDp9judZk6aoGYrzpnMh+JZpZeztS8Gw5EFdk+OM5uqb47qcsMiYhYsWDglsNtE/F5HQWly3iAeS1TEPLpXK5nO+XdiicbGG5XDWatbCfqc7No/Yc71/PmTGil56yWr61rD67azcUULx8ejphm/EozGgGANIiYIAp0tHqbmk4v+Vq8oKjMLKbpaPGxYoQWxHhuLmLlbkm8zALJnjTZSSK3sjRIys+xX1vDVF57llalJNra186mtF5mRCdX20RhhlE/2xPQoquhCtWv7paIiCgID/gB9/iCPJVdz4/71PHmsKGhVjuOYe5ps4DxUZyeyux9BlRAyOSO8Y+EFRGmBdOd1fGHn43zwnjtJqHak4FaThEq+wk5ZKXg+5wmD2AWBPVP15cIZqlu6550ltxUTsa/t3sWOH/wXO0+eqLjeupZWvrJiP2/2Hcnltdm8RM6/nUz327Xz5GxHdq/CEdldEFCr5qX5F+POQwdo/dk834ueVzLeqBwy3Tcx/fohZi/eibdjOzIih6ZKM/euWrWar199LRf19OEf/Ayh3dcXPA/FsOUpYgArAlojRb4qtpBOcXRhLjcrsm5FbDUAYkrznA34tbVPxiqH/Z5KWETMggULpwwhn7OsR6xlCV2TkD/mKPftOZKoT12qBpsocvFZ3cRTEq8cmWFoPMoLB6dY1xfknLX1h5ca5ck9R6qrYgvxDH6PA7ut9kdzV6uHZFpe9OzJ2UgKWVHpbPHQHnIT8jk5Ph7NU8R0IuZdg6BmEVOVA2RFaY5dmTV8eddOdk/m1MOsLPPXOx/n7559quJ9KyliiqvX9Ixdv24j47/zR+z+0G/y5Ps+wk93ePGLGfxzj5r3eXbsJB/75Q/57fE3kdUHhSsureRqy9t3W1ybIiAFz+Pp0RM8NjKEx24n23ppbp/0YzePI3gBblFic9DOqzNTSFXG48SzWb7x0nM8N/wSsqs3p7oVHHNhedUY67OvRuekkJ1FFT1g81bcJhu6ADE7Y45l+vbePWz6r3/nqdHyZeJRXbnqs0drliZN2P3IgbNZ368d2+BCafzD2lAr7w6fRX8ggC15HEGVcSy8UPG4bHo52iBim9s6uLC7h4yS+wJw37GjXPyD2/jJQZ20KXV4xADZswrAnFFqeNtOl1BXi4hZsGDhlCHkd5FMS2T04Fmja3KpiphRfsyPsDC8aIFFJvYbMMqTO/eNm2rYjZevbSiId+v6DgTggV0jZKXKF/FIPFM3cexcomHf8Jd1tngQBIE1vUHmommkhQMo9hYUpzYoxfCJVetSFLLzjMhauaw/zxOUlmXuOXaYf9m9i0eGj5e9e8n6qoyYnjD9YeVw+bnv5ejaf+cNye8xGo3wtp/dzvV3/Ii7Tkb5z8g2xv1XADkilt85aYsf1B7Xu4GxeIzaC3RcAAAgAElEQVRunx9BEMi25HxcUlETgOG9utw7xrmd3cymKp/zkegCn9/5BD+YXUum860FI5rMYzYVMY18Gub/fTOVidiXdz3FbxzZQtxWvVGguDzpdTiYS6cqdk6OGVEbjgSKq+pwnBKsbdfO79FYacdyPgzPn7FPxbDnTT8QpAioKh8/dyv3vvP9nNORK6vmlzsBULMV50zmw1DEbPr0BEsRs2DBwhkLM0tMJ0kL8Qxel72u1PlqMMcc5Rn2m6GIAazs9tPX4eOlQ9O8dHiaDQMhzlrd2EzFjhYPV104wPhsgrufPl52m6ykEE9JNcuSBswssTzD/tR8ksf3lM/NKoYRXWGss6Y3gF3I8v5j23j9iQ/xvf2vsJBO5ak35TsnBWkeAZURSWsQ6fPnOun8TifffNPbcIgin3r4Xm595SXuOXqY4Uhuzp8W52A31xczUwgoBdEV33r5xYJMKdXRiqPnLdgTh+nNvMJEPMabV/TyjsAhAF7IrNTX1olYviKW0LZJe9YxlUyY8QrZlh3mNnJRaVL2b0HyruProdu464br6aoyxNqMrnAskO58a9ltFPcAquA0FbF+f4CQy8Xe6cplz0eGjvM/C6txuloqbgM5IubQSY8RYVEpS8xQxHq9vrKksRqM53o0VVr2fNvPbuftd/4IlDRiVut+NJoIimGL5fx+AgqCXD6A2ejE7NHJvqaI1f6ipeiKmGgpYhYsWDjTUULEYpklq2EAHncZIhbXSnb1EptKEASBS7Z0I+vDyhtVwwy844q1tAZc3PPMEKPTpYOYIw365QwCNZWniN3+8CG+fe8BxmZqD3rOV8QA1vQG6XOdIKXaeCLaxp889hAXfu9WDkmaIlhJETMyxEayGjnp9xdGGpzb2c1fX3ol08kkn33iET5y3y/46cEDuQ1EO7J7pTnTslx0xZd37eRfX3yuYN1U/4cACI5/n8dufg93dX2DW/zahX7frBYQK7u1rKj8CAt7/CCKs5OJrBNFVU0ipjrayLZcguTfYs7AzB2kQKbr7QhyvCAsthwMT9OAM0W27YryGwk2ZO9qfRi1iiAInN3eWdWwfzIWZcAeAWf1krgUMGZQaudifY0ssbFYlKCYwusrN8mwOnp82nM+mnEhSDlSo6oqr85MsZBOm40XQIl3zYDd9CVqBFiQIqQkie/ue5lfHD6Yt686aTSyyZQ0iLUbfRRXD6roMhWxlYEQb1y5hvWtjX2hWi5YRMyCBQunDPnp+pKsEEtml6xYQfnB37nS5NLXv/isHuw2gc2rWtm8anEf3h6XnQ9csxFJVvnufQdQii5I9XZMGjCyxCZ0IpaVZPYd1y628zWaAiBH4Awitro3yCrPCe7v/z6vXu3io2efRyST5hfjGumsNPxb1InYyYwLn8NB0Fl6Yfz4uVt5+OYP8B/XvJUvXHIFV64oTDVXvGsRs9MIUsQMIzVKk9FMmkgmXaC0AWRbL0f2rMY1fgc9Rz6DI7Kby9ds5flbPsrvnn+hvkaRIianEJPDSHpZErSEdgMLW/+H+YvuL3ucad0Q/5OXH+PrRaQwH6NzxwHoa99YlSTInnWI0rw5MH1LRycqlJ1KkJYlppIJVtgXag5WVx0hJO96rQyoqvgdTgb8AQ5VyBIbi0W0Yd+e2kb9YnjsDr6wZo7fDO0uUB0jmTTxbJYBf7CAiImZKS1Gogj22D5UwUY2tA3QIywEgT97/GG+9UqunDkej+EQRdrcHlBlBJS6uiYRRGT3CtMj1un18sPrbuKDZ53b8DEvBywiZsGChVOGoN/IEkvnKUBLi66API9YUWnSJgqLzhHLR3vIzV9/dDufvOnsJa2zdWMnF27s5OCJBZ4oKiGafrk6ozzagm5EQTBLjIPD8+aIq4VE5RgEA1PzKRx20cxw83scbG7R9qmnfTN/eKFWqnt6agFV9CJWUMSMVPykamfAH6yoFp7T2cU7Nmzik1u3cUG3RrKMTr6cYf9YiSJm5Gf1+4uGwgsiqb5bEJQEntHvaV2e5/xfVoZazH1QXD2oCKZHzJY8qpU9fRtxijauXrmac/LCWVV7ANVefvi8FDgf2b2Krw05+cfnni6ISsnH2Iye4N53WdnbDeR8Ytr2t2w+h59c/042t3eUrqkrQSvsETNDrBqk4FZEaQFRD43d0NrGeDxOJF1I0FVV5R+3reL/tD2GXK9Rvwh/Gvbz7sC+AoJllPz6/AHz+TRKzSXlSVXFFtuP7F2P6tSeC0GK4LDZ6PH6Cromx+Ixenz6AHMju6yO0iRo5UkxO1ug3J0usIiYBQsWThny0/UbVYCqweyazEvXj8QzBH3ORZURy6G33Yd3CZlkBt5/zUacdpGHXihUBho9H3abSEfIbZYY9xzJKR7RePVOSlVVmZxPmkZ9A70hmaxiJ+U5ix6fn/UtrciqqpfRjpUtKxmlyd1Xt/LIuz9Q177ftncP533nP835ikaEhZg8VhLmaniuikueAKm+W1ARUGx+Iud9D2w+klKW58dHtfwp0Yni7DK7Jm1xzR8mezdwTmcX/33dO7g5XJrOXxaCQLr77VzoPEFCkjg0X77UNxmdQUClY8W1VZcrNuxvbu/gyhWr8DtKn/9RI8y1DkUMQApeAIBjQSM97920hT/ffimyWkgeBUHg5u4ENwderZ0hVuk49Pvld6Ya+zsQCJjdkBndL+coImJi+iSitIDk34Kik2BR0jyE/YEgY/GY2aX69auv5R+u0AbyCDoRq0sRIy/CQlfFfnpwP59+7CHSUvVGg1MBi4hZsGDhlCHfI2akyDfDI2aUJuNFithSOyaXA60BFz3tXqbmCjPAjHDbYAPno7PVQySeIZmW2JOX3B+poYjFUxLJtGSWNw10X/GPHN3yEHbdL/T4ez/M7de9A9mzRk+/Ly1vGURMcbTisNXXdGEXBcbiMTMcNb8hIBfmqhOxWE5dKYbiHiBy3g9YuOAOZN9GAP7xuWd4689u58XJ3OBwLdRVxW50TPo21LWfxUh33cCFbm3dl8vkiQnpCR7s/ldObn0cu6tU2cpHcYSFgUyZWZaiKHJxu5uznFOozuqjkECL24Bcl+JNGzbxJ9suptVdGkJs05WsxRKxb4042TH8cY7O5L5Y5D9nBrHOdFyr71MhEbNHNaO+7N9sqpG5LLEAsqqa3ZKXD6zkjau084aqf9moI74CSiMsHh0Z4ruvvmwS/dcSFhGzYMHCKYM5+DuWycsQW3pp0lDEkjoRS2UkMlllyUb95UJnyENGUogkcsrVYhRCg0i9fGSG6YUUA52a3yl/jFQ5TBUZ9Q24vS20DVxg/m4XtUuEGTFRpnNSzM5yUgrw6Iyd6WR9Sf/ndmgxCUY4an6oa3Fpcj6dQkBTV8oh03UdUl7H47l6irxBlBRXP4KS1rKq9I5JybeBHx14lX9+4dmS5PZqkELbOMuvEaVDs6VRE66p+xAFlUDfNTXXKg51BXjTT37Ahd/7Vsm2F/f288Blbm4JvlKfIhY4BxWxYlyEgZ8e3M8Fj8ncE9+w6NLkpORlV3qAE5GcQrixtY2PbDmPLe2d5vMp+Tfp3rWXCiYdGEGukn9LCREzytEnYqVkyVTE6ixN5ojYcQAGdGI/vLBQ6S6nDBYRs2DBwimD12XHbhMKSpONKEAV13UXmvUNghNqglF/OdAecgMwvZDreFwUEdM7Jx96XgvrvPxcTUWqRcQmTaO+u+p2kqJwx6EDfGdaLz8lS4mYkJ3j/vh6bnrsIA8crz4c3MCm9nYcosgr07oi5lmFioAtcRRbegzFFkC1axfK379gOyO/9Qfs6O2va+1zOzWSZxIxI8IifRJb/CCq4ERxr+LHg/v4u2efwtZI6VoQ6e7V8sYmZktnXWbH7+XZVD9jgTfWXEpxD2idfHnk1udwMJGIk5ZLy2WGF09x1FbEsPuR/Zu0CAtZS6P/wN13cusrhcTs2MI8B5MO7Cgo7vrObzF6WrTX3Fg8R5Yu7V/BV668ms3tHQXjqqTgBYjSQgH5NEYbSf6zSojYQCCo+SATce4/foRN//Vv/Pf+vdodVf01XmdpUnGXj7CwiJgFCxbOKAiCQMjnJBJPm+N8lpqqD+By2BAFwZwvaRCR01YR05Wo6fncrLuFeFprLmhgJJOhiB0ZjSAA28/qxm4TapYmJ4syxCrBJgh87snH+NJBGVUt3zkpZucYkbQLaLnyYTm4bHY2tXWwb1pPqbe5Udz9piKWH10B2sxEQ52rhdXBEEGnK1f2dOkRFqmT2BKHtZKgaGcsHqPd7cFtb6yZo73vKlyChJop7W48MHmci0d+k38+UHseJYKoj486anrvjPNnmPMN/Pf+vXzzaBxFFeoy6wNk2t+IoCRwzj6GTRB5YOgoL0yMF2xjes88SsUmhVroDWhdxGOJ8nMbxfSYpuKJLqSQUTLNlSftsf2oohfFsxrVrmXRGUTsfZu2MPKJ3+f6dRsZjcWYTaVw6uVvQdHe6/Uk60OZdH1dbbOImAULFs44hPwu3SPWvK5JQRDwuu1m12QzoyuWA2UVsZjWXCA2oNB05hGp1b1BQj4nQZ/TzFCrhOLoikoQBIEdvX2MJrMMSS1mWScfYnbWDHMtZ6ivhHM7u0jJMgf1WAXZswYxdRIxO1OQqv/kyeGKYaSV9vnczi6OzM8Ry2RMRcweeRFRipheMqMDr1E4fatJrvsSt208XniDnOJESnvu6j0PsnctorRgeu8G9PmKo0WluH976QX+z2EvoqCaA79rId11vba/k780877GitY1Sn4rQtVDYquh16cd68mkYhLK33/kfv7m6ScAEFNjZoxINqjFiphETMliiw8i+TeBIJaY9d12u+k7NHxiBRliULdHTHW0odgC2Mx5k5YiZsGChTMUIZ8TSVYZnYljtzUnXgK08mSuNNm8jszlQKdJxDQVQVVVs8uzoXXyiNR567QLdNDrJJLIVB20PTWfRAA6QtWJGGCWBB9PripLxITsHCOSdiGvVxEDeOua9fzRhTsI6LljsnctAto+G4qYqqq875d38MmH7q17XYBzOrpQgb0zUyi6Iuac/RUAkm8jUT3nqncRREx29yEIhSGxoCk/hjJYErVRaa0in5hx/vI9UaqqMhKNsNKlv1bqVMSk0EXIzm5cU3fjEqDT4zVT9A2MRhdoFZP4A4srS0KOGI1KPoTsLIqq8rODB3jq5AiCFEWUo+bzKQXOQRVsZjenLX4AQc0g+bdox1ZUmlRVlX3TU7wwMZYbb5SXqg/1d00iCCieVfoAe5W+QIAWl6ux0vQyoTmfgBYsWLBQJwxyNDmXpD3oalq8hNdlZ04PMj3dS5PtRUQslZHJSErDxNHlsNHidzIfy3Deeq1LL6gP705lZDyu8h/xk/NJWoMuHPba38Uv1onYk9mzuCXxaMntYnaWEbmFFpcW6Fovrlm9lmtWrzV/NxoCINcxOZ1MkpZl089TLz5y9nm8K7yZTa3tyHp2ln1BC2GVvetzCe3+xokYNi+vymvYO27jYimLx64dsy09xrCuDFZqLChGfpaY1LLDVGnyFbG5dIqElGWVP4oqOFBtde6zIJLpug7PiVtxzD9Dnz/A4Ow0qp7kr6oqJ2NR1tsXwLey9noVEHK5eHN7igvVUWypE4xLbjKKrHdMaqVQc1yVzYvsOwt79GX8+/8Y9+gPAa0JAvKIWFYjYoIgcOPPf0yP128+VwVzJqFuRQy08qQ9thchO4Pf2cHBj32Szs4AU1OvbeekpYhZsGDhlCKfHAWb0DFpwOu2k5UUspLctIHfywW3007A62Ba92otJVPt3HUdrOkNsrJbu0AF9XJsJZ9YJiszH02XRFdUwtkdXfgcDp5IDGDLjIOc1xmpSAjJk4xkgw2pYeVghLpCfpir0T3X2NprQi2c09GFw2YzSZ2gamqp7NvIQiZNyOVaVGkS4KsLr+M9xy9jOJILGxXTowxnS+dtVkOxIra5vYPPbL+Uy/pzHYxGoOkq25xm1G/gi0u66zoAnFN30ef3k5JlZlMa+ZcUhQ+uaeHdgX3g6at7zWIIgsD/7PDyhfZfIaZOMpqX+1YcRQKQDV2AoCTxnPgWirOd2IYvmeOqjAYNQxEDbUD3iViE8XiMkMuFVyf7jXZNQmnn5OkCSxGzYMHCKUW+J6ylCR2TBoyw1URaznVNnqaKGEBHyM3IZAxFVc3GhcVkqn3kLYUDqgM+7TxE41m6y0xjGp9NoAI97ZUHV+fDLopc1NPH+EycpGLHlhxC9m8GtAuaoGZ45sJhptb8RsP7/uVdT/HI0HHufef7UDw5dayeDLFakBWFkWiE1aEWFEeb2XUoezdwUSjEoY99smTMVL3od2u+pbHoNOE2rSQspjRFzC0KtJfJ6yq7j14tz8we02Zv9vkD/7+9O4+PrKzzPf45taWy751Op5vuprt5mq0BQUAUUUBBL4qow4BeRQfH0cts1xkXxntn5t6Xc4XXLOo46h0vI+IMDDqoCG4MMo7ryMi+6dM0DfSa7nR3Oktlq6pz7h/nnEolJJ2k6iRVnXzfrxcvUqcqJ+fJSVd++T2/5/fwoXPOn/Ka3UEgtiHehzfHPpPTZVsvxE20UHPw25zd9TayebewIjMZj3PzqQmanv4J1F63oPNOF/Ygi43tZm92S2EssXF/14DiQGx03ftxcsOMd72Zic43QawoDHHiuPFGYsWBWGMjTx/u4w0bN09dsLHAVZMwufl3fPRFcs3n8MzhPl7ct5MLO9fO2Eh3qSgjJiJLqjg4ijJQKuw3OZZlMDOBAzRUaUYM/PqsXN7ze6oVMmLlZwjDlh0Ds7SwCDcc7+mYXyAG8E9vfAu/vKiG2lhuSjYhPvIsjgPrO07k1KKtguZr79AQj/UdYOfA0SkZsXxQYL9vePau+nP5zW9/g/PvuJWRbLZQJ5ZPdeElmwuvWcjCiGLd9X6g1ds/2U0+Nr6P27ru5o7XnD7v6Xa3ppt8qovE4MOzvubo2BiJWIwNsd75ta4oFksx0XkZ8bHdfGhzkjuuuGpKUBsbD5rSprsWdt5pvn+4nj/su5y+wX1TuurPlBHLN57O0LZbmei6amoQFvASTVMzYsEChis2beHDL39F4fhkRmwhU5MbgMkWFl95+gmuu/tunh84Ou9zLAYFYiKypKYEYhGsmAwVeomN5RjMTFBfmyQ+z5YHldBRtHIyyu2eGuuPPTW577AfiK1pr5v3OVPxOPm6DcDUpq7xjGXETTJYs7mkaw2brz7edwAv0YSb9OvcXrrP5MIDsU0trbie5wd5QWAXdtR/9EAvP9mza8Z+XfPRHRTjHxiabOoaG9/PaTUHefWGeW6ZBOA45JrPIT6+n9iYX/x/40/+jTd986uFl7zzlNPZ855rubLh1/Mu1C823hmunrx3yvH7XniODz42whPjXZBePdOnztvPjrh85uj5PH/0MA2pFKd3rGJ9U/NLmvPOhx+ITa5kDO998Z6T/knDGrFSpib9QGxLazvburpm3M1gKVXvu5SILEuLnhEbzzE0MlHV05JQFIgdHYt0cUF4jqFZMmJ7+4JAbAEZsfF8jrv76rkvs4lYcUYss51/HNpGz907+Mazv17wtZ4+rflqvn4LnpPATfm/uG848+Xce9VvsnWGjbDnsiZoq9CbGZrMiNX5rSs+/ciDvO2euxiemH9X/WKrG/3r2T88mUnJje5nwkvgplYt6Fy55qClw8BDgN9k9cH9exmemLx/ydxRko678IwYMNFxCV4szcDe+/n0ww/y/ef9erRf7t/HP+2vZcCtKTsj1t3s36/e4SHecfJpPHD1f2VbZ9dkRiw9/xo0L9GEkx8qtMJYGwS9133vHr73/I7C6xwvXDU5/z/m8ml/UUKY1b3+9DN5/AMf4Oyu7mN81uJTICYiS6q4DiqKfSZDYRuMwcwEmbFc1a6YDHWETV0HRgvbPUXx/ShMTc6aERuhPp1Y8PfnPT9+ipv7X0V8dLKpayKznV05vxCtq27+gV3otI5OHCg0Xx3a+pcMnnE7xP0gtbOujvO6e0qq3wkL8fdnhguBQL7ez9z1ZoapicdpSx97Z4FZz90cTJ1mMoVjdx2opX7Hjdy149kFnSvb5K8YTAbTk2EGKMwG/nTvLp45uAsAd4E1YgDE65lov4SxzC7+z4M/49s7n51y/hMSA5BeWPA43eogWNo3ralrfHwfHjHc1Pynrd1EE46Xh7z/vb1w7Qlcf/qZgH8vJ1+4sD5iACQacJMdhanJaqFATESWVDIRL2SvoqiJCtUGgdiBfn9VX7WumAyFGbG+gbFFmZqcKSOWzbkc7B9hTUf9gtqG1MQTrKqrZ1eudbJGzPOIZ7azy/ODklIK6huSKXoaGnlx0J+KyjduY6LzDYXnB8fHj9kP7VjCdge9mQwT7ZeQr93ARMfrAf8Xeld9Q8mtUxobe3hk3f/liybooO957BiJkSNeaJ46X7mms/BwChmxyUDMn4p7333f5vqfWf/LlDA1Cf72QT1x/3xhDde+zBAOHqvTcYiX9+8wzD7uG/P4wmO/5N7n/M3VY+O9uDVd4MxvM3iYbGERFuy319YWsmLFfd8mO+sv7N95vnY98bHd4FV2OrKYAjERWXJh5ifSVZM1/hty72E/EKv2jFh7kx+IHR4YYyAzTk0yTjpV/kL2xtokDjPvN9l7ZATPW1ihfmhtQyO7c404Iy+C5+JM9BHLHWV33p+mW1NKTy7gTZtO4nXrT3zJ8ZzrctKXPs9v3Pv1ks4b/tLuzQyTaz6bI696gnz9SeRcl4MjIyU1cw256R7OSveyyvVroJzsEZ6d8Jvantg8w1LVY/CSzeTrT/I36PbyhWawe4eHGM5OcGRsjHW1QaPbEgMxN91DTSxPZ02sEIjtHRqiO5Ehnl74IovpwqD3xWwzf/7zn3DLk4+C5824XdVcpm9zBDN01YeSVk2CH4g5XrZQv1YN1L5CRJZcZ0stRwbHIw2WwmL93iN+IFbtNWKpZJzm+hSHBkbJltDMdTaxmENDXZKBkZfWP4UrJrtLCMR6Ght55GCcvmyS5Pj+QtH+nmwDHbV11MRL+3Xyv1550YzHezN+a4+O2vkvKphyvQ2N/PVrLuW09qnTbn0jGVzPKysQ81Kd5EjSO3SYGtclNb6P7dl26mJuSb3Jcs3nkM5Y4hlbaAa7d2ioUKB+Qk2Q/Sk1EAu2GOqpcbGZYfKuy/7MMGen+nFT5dWHgd+1vznh0ptvwMPPkDm5fhx3vPC158tLht31Jwv2//4JvxP/6ikZsYWvmoTJhQOxiUOFthuVpoyYiCy5d73e8LF3voxEPLq3oPrC1KTfJLVa95ks1tGS5sjgOIOZLE0RZgeb6lMzTk3uPbTwQv1QYZPkXDPx0ReIZ7aT82K8OBZjfVPzHJ+9cHvKWDEJUJdM8q5TtnFW19SMTLjNT6nNXAFwYry372o2PHEJe4aHiI3uZftEO5vqnZJaYhTqxAYeYkNTC5eesJENzS2TzVxT/s90qRmxfBBw9KRGGc3l2D00yOltLZxZ07ug+q3ZxGMx9lxez80d9/tfp6GR2NjCV0xC8TZHk93uTwh+vjqLg/Jw1aSzsKlJL+lnLJ1s/4I+bzEpIyYiS669OV3Y5icqYd1ZNucC1T81CX4vsef2+r9so8zgNdWl2NuXIZtzp2xjtL+EHmKhwibJ2Wa2jjxPfGQ7ec/h86/YSqJxS8nX+siB/dz562d4+0knc273ZPZkXxnNXI9lW8cq/uMd7yUVn3/d0ky6037AtX94kMbMPka8FJsaS/uZLl452dPzbu644ioAbn3qcQBOSPSDi19vVYJwscK6xCAtNS3kXJcfXH4GrQ/+PiM1/62kc06XT69ld7izQGNjUeuKha1IdKfViAH8+9XvYiSXndKOprBqcqEZsSAQCxv8VgNlxERkWaibtnl4tU9NwmTBPkQciIUF+9NWTu47nKGuJlHS17pm66nsuOoM3tbwDPHR50lkLDWxPFed9iqu2FR6ILZ3eIgvP/04jx3sfclxKD0jBvCRHz3Attv+fkq/sGQ8zqaWVtY1zm9j7tmsqfML3HsH9tOQ7+XvV93LOzaXtnl2ruFUvFia5MDUxq67h/zpuY1xf1FAqdOIXqIVL1bLp7p/wfbrb2Bzaxux8fCc5a2YDP1qrIW/OnoBMHV7o/xCpyanbfwN0JBKsWr6qtxganJBqybxvxegjJiISOSSiTjJRGwyI3Y8TE0uUiAWrhgdHJmgLVgUkM25HDgyyolrmkpaLdhUU0Os5SQcB+KjzxPPPEu+prvwi7NUYa3WvszUjZfDabmFbvhdbDg7QW8mw4FMpjC9dXAkQ1OqhnSivF9/hcUAA720Ne3n/c0Pc2TjyZS0Fi+WJNd4hr8xeT7Dnc++wKMHe/nouRdwxYlbOGv7XbheS6Gtx4I5Dvn0GlIT/k4AD+7fy86du7g610BdiVm26W59fphHx/3s19o0xEdKnZp8abH+TBwvXDW5wIxY0AIkVkWBmDJiIrJshNOTAE311d2+AiZ7iUG0uwyEQV3xyskD/SO4nldSfRiA53nsyzdhs10khp4mPrabPz70Ri668yvsGhyY+wSzCKce9w8PTzl+1Zat/M9XXMjG5paSz91d1Ess9MH7v8uG//dZxnKlddUPrW70f6H3Dh0pdMVf6DRcsWzzOTi4JAcf574XnuPWpx4n73q8rKubxvy+kqclQ25ND2Nj/fxk904++eDP+IPHh9mTa8KLoEYMYHWTv3r2q6v/hXP33UhsvLTvyWRGbI6fqbCP2AJXTSojJiKyiOrSCQYyE9TWxEkmyqsBWgrFGbEoa9rCbOBgZnLl5L4yCvVD591xG1uTV/NI8rMAPDHeya/6D9FWO79Nrmeyqq6emOMUpiJD53f3cH53aVN9oeIWFqEXBwdYVVdXfkasuQvopTczxPsP97Bj9Le5w0tT2hpPf+UkBHViDZqPCcMAABpySURBVKcDfqPbi9euIZY9Qq7x9LKu102vYV+ukbfde3fh2LrEQCSrJmHye30gfQZ1h+/Ai/k/EwvNiLkJPzCfMyNW6qpJ1YiJiCyeMCN2PExLgt9LLJwkjHRqcob9JicDsdJCBcdx6GloZHd2cipyx1gtq+vrS+p8H0rEYnTV1U/tmh6R1dMyYtl8nj3DQ6xvKj3LFmpvXMvfdX6H3+kZ5D+Hm/jVRAe1ZQR32aBgPznwUGGF6rXf+Sa/ee9dQPm1XG5ND2sTk8FNjePSGR+JrEYszGz+MPZ68ul1OO4oXqxmwS03pjd0ndUyWjWpQExElo26tP+m3HgcFOoDJOIxWhr9KckoA7GZpib3FVZMlt62oaehiUO5JCNuklE3we5Rj00LbGA6k61t7bSm04Uu+jsH+jnv9i/xhccenuMzjy3sch9mxPYMD+F6XiTtNpzabm5o+SUX1OzkuYlmtqTHS+7UD+Cm1+MmWogPPzVlgUJ3kDRdaGZpuny6h3QsR3vK/7XfkxrDccBNLXwfz5m0p/0M2Dd3vsDgGf+EF6vxN9le4PdkpmL9mZS6atJLNOM5cWK56gnENDUpIstGuHKy+TjJiAF0t9cxPJqNdGqyuFg/tO/wCLU18bJ2Mwibje7ONZH14njAiS3lB2JffdPbpjzeebSf5weOll3Htb6phXdsPbWwqXO4lVIUgVg+aAmx79B2spzK5vrStmIqcBzytRtIZH7NmqIeZyfU5CBHYSP0Urlpf5q3M+VyeMJvZeEl22CBWwTNZnNrG9dsPZVLT9hIrukkjr78X/EWWL8F4MXnF4iVumoSx8FLtFRVRqysQMwY0wZ8FdgAvABcba19yeiMMXngyeDhLmvtm4PjG4E7gXbgYeBd1tqZd6oVEZlDYWryOMmIAbz7MsNgJhtpc9vJGjH/7TSXdzlwZIQN3Y1lZW16inqJDTp+MLPQLX3m4/mBo/65W8qbQuysq+PTF19WeBxlIEa8nj858gY+efg8ADY1lr/Ywq1dhzP0GOvSk7V961MjfiBWdrG+HzgmyAJJGp1M2ecsFnMc/rboe51rOqvEEyXw4vXzXzW5wKlJ8OvEllON2MeAB6y1W4AHgsczGbXWnhn89+ai4zcDn7LWbgb6gevLvB4RWcHCjFi1b/hdbFVrHZvXRtuZPpWMk07FC8X6e/sy5F2PNe2lF+oDrG2c7K7f09jCe087Y0oT1lLtGRrkX+wz7Oj3fzmGgVg5KyZn8uq1J/C3F19W9iKA0JAzeX0nNkeRZVsHQE/sEB86xw/wTkj634tyg6Z8kBF7ZYOfK/loy48iqw+LmptoIjbnqskSM2L4dWJOth9K3FA+auUGYlcCtwUf3wa8Zb6faIxxgIuBu0r5fBGR6QpTk8dRRmyxNNWnClOT3/rp8wBs21RePdCFPSfwj5e9kdd1epy+4QJufvUlvHx1+YHYwwf2c8MD3+dHe14EYGcQiG2IoKj+c48+xG/f9208z2NjcwvXbD210FOsXGtq/Z+31tgop3aUN3UI4AaBWGxsN3uG/FWkG+KH/OfKnJr0ku14Too/7nicX7z1cs5L76naQMxLNC1g1eTCM5Fusg3Hy+Hko18gUopya8S6rLXhFua9wGwhe9oY8xCQA26y1t6NPx151FobFgHsAeb1Z0prax2JRVqa3tkZ7XYaxxuNf+WOfzmM/ayTV/PNH+/k7FO7Fzye5TD+Yu3NtdgXj7D7yCiP7TjEqSe2c9krN846NTmf8Xd2NnLmiWuARwEoL7826ZQx/1fHUXeCzs5Gdg0P0llXx6a15ReSP3K4l3uf286XGt9yzDYbpdz/9S1NsBv+ov0BXr7lrdBR5s/Q2EmwHZrjB/m9C36DCzasY8voJwFo69kMqTLPX7+WzfmdbO524EmXZMta0sG4q+rnP90KI8/R2dEwe7F/wm+d27mqbcELAmhYBYego8nPGFd67HMGYsaYHwAzheIfL35grfWMMbPl+dZba/caY04E/s0Y8yRQcgfA/v6RUj/1mDo7G+nrG5r7hcuUxr9yx79cxr66qYYvfvi1AAsaz3IZf7HaVBzXg89+9VEc4O2vPpFDh2bOACx0/K7ncf1993JyWwcfOfeC8q8160/OPHvwMH19Q7xx/SZcvEjuSVvSX3b45Iv7eff37uHMzi5uueyKKa8p9f631fih6J5cE4dHm3HLvN7ERAetwMihZzEdrZhNrTgP7saL1XDoaAyc8s7fnOgmOfxzBnufpRkYzrcy2jdUdT//zdST8nL0HTgI8ZnbrbSMj5JwkrP+TB9LvdtAHdDfu4vWTeuXZOzHCvbmDMSstZfO9pwx5oAxpttau98Y0w0cnOUce4P/7zTG/DtwFvB1oMUYkwiyYmuBvXNdj4iIzC1csHBoYIwLt3WzfnU0f/W/7l9upzczzIGRDKNlrmoMddXV4wD7g22O/scrLozkvDDZaNQeOcyuwQG2trZHdu7Ohk4gw98ePY8/jGCaL58+AYD42J7Csdj4AX9asoxFFiE33YODR2LI30y8Wqcm3WCbo1huEHeWQAw3W1J9GFRfd/1ya8TuAa4LPr4O+Nb0FxhjWo0xNcHHHcArgWestR7wQ+Dtx/p8ERFZuKZgwUI6FeetF22K7LwxBw6M+D3JoughBv5G3Kvq6tk3HH3NThiI/WK//3d+JCsmAxvb13Jtw5N8vuenECu/G5SXbMOL1REb2x0ccIlNHIxsdWPYwiI56E8tuzXVGYjNp5eY402UtGISqm+/yXIDsZuA1xljngUuDR5jjDnHGHNL8JqTgYeMMY/jB143WWufCZ77KPAhY8wO/JqxfyjzekREBOgM9rG84oINkS5eCLu+Q/ntJYqtaWhg//Aw9z63nd994Pv8+sihSM7bFQZi+6IPxJzaNdzR/XWu6cpEdEKHfO064qO7/IfZIzheruxmrqF82MIiDMQi2t4oavPab9KdWDYZsbJCeGvtYeCSGY4/BLwv+PjnwIybZFlrdwLnlnMNIiLyUued0kV7U5qTToi2BURP4+QUZ5Q9xL7yhitpSNXwyQd/ytfsM1x/2pmRnHddYxMntbZxeGwUgPURtJkI5dMn4Dlx8rUbIzunm15HImNxckPExnv9YxFNIYYZsVj2cKTnjdq8MmJudsFd9UPVtt+ktjgSEVmGEvEYW9e3EougtqjY2qKM2KYIuuqHuuobqE8mI+8htqW1jZ9e+x4u23AiEE1LjJCXamfgZfeQOekTkZ0zrBOLje0mNnEAKH97o1DY1BXAI4aXiq5eLkrufLY58iYWvM9k4VOrbL9JbXEkIiLzVrwPYvHH5RrP59g7PMQjB3ppS6dpSacjOzf4PdDAz5BFKdsW3cICALd2LQDx0V04YeYqqqnJ9GSHKC/VAc7itIEqV2Hj7+yxMmIThaL+hXKDjcirZb9JBWIiIjJvp3Z08gcvO5dL128kHotuUuU7O3fwgfu/C8DZXdEEHqH7X9jJ4bER/vo1r4v0vIshX2jquqeQEYqqlstLdeI5SRwvW7X1YeBvzA1zZMTciZKnJpURExGR49bG5hY+fv6rIj9v8UbXUU4fAnzusYf4j317ePcp20jGqzMLFJpsYbEb8n5dW2R7Qjox3Jo1xMdexK3pjOaci2A+xfpOOVOTiSY8YqoRExERCXUXTXNu64w2W+N6Hh7w2Ud/Gel5F4NbG2TERncRm/Bbc0Y1NQngpv06serOiM1RI+Z5Za2axInhJVuUERMREQmtrve71L+qZx0fPPPsSM+dyfpb2YS9xKqZW9ON5ySIj+3GcxJ4xHBT0WWv8jVrSFK9KyZhslg/NmsglsfBK3lqEvw6seXSR0xERKRsNfEEHbV17BuOfruZk9v9PStP66je6bgCJ45b0+OvmhzvjbyoPmxhEdl05yKYOyPmb/hd6tQk+HViTrbfz65VmDJiIiJSFQ6NjnBodIS860a6EOCmCy/m9I5VvOe0bZGdczHl02tJHv05xNLk6rdEe+7aDcHX6Dn2CytorkDMcf1ArLyMWCuOl4VcRM14y6BATEREqsLfXXI5Tx3qi7z3WUMqxe+c8bJIz7mY3Np1OEc9cEcjr+UaW/NOiKWY6HxTpOeNVCyJF6s7RkbMn2r2YjUlf4lw5SQTh4G2ks8TBQViIiJSFa42p3C1qfRVVF7YwgKiLdQHIF7LWM+7oz3nInCTrcRyR2d8LsyIlTM16QbbHDF+hEoHYqoRExERqSJu0MICqruWazEVarhm4o77ryljatILmroyUfkWFgrEREREqki+tigjVsVtJhaTnxEbADf3kuccNxt8UF6NGKBATERERKZyF3Nq8jgRZqycmaYnvbBYv7xVkwCMHy75HFFRICYiIlJF8um1hY9XaiAWZqxm6vVVqBErc9UkoIyYiIiITBOvLTRxrebGq4tpcj/IGQIlr/ypyUKN2LgCMREREZkm3HNypRbru0GgNNN+kJN9xMpYNVlFGTG1rxAREakymc1/RnzkOYjXVfpSKuKYGbFCIBZBH7EqqBFTICYiIlJlsu2vIdv+mkpfRsUcs0YsiqnJRDMeMZwqyIhpalJERESqSmHV5Ey9xAp9xEqfmsSJ4SVbqmJqUoGYiIiIVJXJjNjsNWLlrJosfA0V64uIiIhM5SXCGrEZMmLhXpNlTE1CUCc2cRg8r6zzlEuBmIiIiFSV+fURK2NqkmC/STcL+UxZ5ymXAjERERGpLvE0XqxulhqxcNVkmRmxVNgiY5Y9LZeIAjERERGpOm6yddFWTUKQEQOcnAIxERERkSm8ZNvMQVJUGbFw+rPCKycViImIiEjVcZOtxHKDfh1XkchWTQb7eDpBO4xKUUNXERERqTqF7vq5o3jB3pv+E0FGrMypyfHVb6exuZmJuovKOk+5lBETERGRqjO53+TU6cmoVk16iUY48TqIp8s6T7kUiImIiEjVmXW/yYhqxKqFAjERERGpOrP1Eotq1WS1UCAmIiIiVWdyv8nZMmLlTU1WCwViIiIiUnVmz4gFNWLKiImIiIgsjtlrxIK9JmM1S31Ji0KBmIiIiFSdxV41WS3K6iNmjGkDvgpsAF4ArrbW9k97zWuBTxUd2gpcY6292xjzZeAiYCB47j3W2sfKuSYRERE5/k1mxKZ11w8asC6XVZPlNnT9GPCAtfYmY8zHgscfLX6BtfaHwJlQCNx2AP9a9JIPW2vvKvM6REREZBnRqsn5uRK4Lfj4NuAtc7z+7cD3rLUjZX5dERERWc5iNXjx+hkyYmGx/vLYHKjcUXRZa/cHH/cCXXO8/hrgb6Yd+wtjzJ8CDwAfs9bOuelTa2sdiUR8wRc7H52djYty3uOFxr9yx7+Sxw4av8av8VelmjaSbv/U64vnIZaic1VTJF+i0mOfMxAzxvwAWD3DUx8vfmCt9Ywx3jHO0w2cDtxXdPhG/AAuBXwRf1rzf891Tf39i5NQ6+xspK9vaFHOfTzQ+Ffu+Ffy2EHj1/g1/modf2ushdjoCxwuur6WiTHiTmrKsVIt1diPFezNGYhZay+d7TljzAFjTLe1dn8QaB08xqmuBr5prS1so16UTRs3xtwK/PFc1yMiIiIrg5tqIzH8pN+yIlgl6bgTsEwK9aH8GrF7gOuCj68DvnWM114L/HPxgSB4wxjj4NeXPVXm9YiIiMgy4SVmWDnpTeAtk0J9KD8Quwl4nTHmWeDS4DHGmHOMMbeELzLGbADWAT+a9vm3G2OeBJ4EOoBPlHk9IiIiskzMtHLScbPLKiNWVrG+tfYwcMkMxx8C3lf0+AWgZ4bXXVzO1xcREZHlq7DfZK4oI+aO4yUaKnRF0VNnfREREalKhYzYxOQ2R443sWx6iIECMREREalShe76UzJi2WXTVR8UiImIiEiVmmm/SX/V5PLYZxIUiImIiEiVmtxvMpia9DwcrZoUERERWXwvWTXp5fz/x2oqdEXRUyAmIiIiVSmcmiz0EfP8fSY9TU2KiIiILC4v2QJALJiadAobfmtqUkRERGRxxWrw4vWTGTE3zIgpEBMRERFZdG6yrVAjVsiIaWpSREREZPG5ydaiVZNBRkxTkyIiIiKLz011EcsP42T7/X0mYVntNalATERERKpWvvE0ABJDTxZlxDQ1KSIiIrLoco3bAEgMPVFUI6Y+YiIiIiKLbjIQexyCqUn1ERMRERFZAvm6TbjxBhKDT+C44/5BFeuLiIiILAEnRr7xNOIj23Fyg4D6iImIiIgsmVzjNhwvT2LoMf+AAjERERGRpZFrPAOA5MBDgFZNioiIiCyZQsH+4CP+AWXERERERJZGruFkPCdJTDViIiIiIkssliLXcPLkY62aFBEREVk6+cbTCx8rIyYiIiKyhMI6MUA1YiIiIiJLKVw5CVo1KSIiIrKkcsHm34AyYiIiIiJLyUs0kas90f9YgZiIiIjI0so1BdOTy2hqMlHpCxARERGZj7G1v4WTHyXXcEqlLyUyCsRERETkuJBtu4hs20WVvoxIaWpSREREpEIUiImIiIhUiAIxERERkQpRICYiIiJSIQrERERERCqkrFWTxpjfAP4cOBk411r70Cyvuxz4DBAHbrHW3hQc3wjcCbQDDwPvstZOlHNNIiIiIseLcjNiTwFvBX482wuMMXHgc8AbgFOAa40xYQOQm4FPWWs3A/3A9WVej4iIiMhxo6xAzFr7K2utneNl5wI7rLU7g2zXncCVxhgHuBi4K3jdbcBbyrkeERERkePJUjR07QF2Fz3eA5yHPx151FqbKzreM58TtrbWkUjEI73IUGdn46Kc93ih8a/c8a/ksYPGr/Fr/CtVpcc+ZyBmjPkBsHqGpz5urf1W9Jc0t/7+kUU5b2dnI319Q4ty7uOBxr9yx7+Sxw4av8av8a/U8S/V2I8V7M0ZiFlrLy3z6+8F1hU9XhscOwy0GGMSQVYsPC4iIiKyIixF+4pfAluMMRuNMSngGuAea60H/BB4e/C664CKZNhEREREKqGsQMwYc5UxZg/wCuA7xpj7guNrjDHfBQiyXb8L3Af8Cviatfbp4BQfBT5kjNmBXzP2D+Vcj4iIiMjxxPE8r9LXICIiIrIiqbO+iIiISIUoEBMRERGpEAViIiIiIhWiQExERESkQhSIiYiIiFSIAjERERGRClmKvSarnjHmcuAzQBy4xVp7U4UvaVEZY9YBXwG6AA/4orX2M8aYNuCrwAbgBeBqa21/pa5zsRlj4sBDwF5r7RXGmI34m9K3Aw8D7wo2ql92jDEtwC3Aafg/A78FWFbI/TfG/HfgffhjfxJ4L9DNMr3/xpgvAVcAB621pwXHZvz3boxx8N8P3wiMAO+x1j5SieuOwixj/0vgTcAE8BzwXmvt0eC5G4HrgTzw+9ba+ypy4RGZafxFz/0R8FdAp7X20HK79zD7+I0xvwfcgH+fv2Ot/UhwfMnv/4rPiAW/jD8HvAE4BbjWGHNKZa9q0eWAP7LWngKcD9wQjPljwAPW2i3AA8Hj5ewP8JsMh24GPmWt3Qz04/9jXK4+A3zfWrsVOAP/+7Ai7r8xpgf4feCc4I05jr/jx3K+/18GLp92bLb7/QZgS/Df+4EvLNE1LpYv89Kx3w+cZq3dBmwHbgQI3gevAU4NPufzwe+I49mXeen4wz/IXw/sKjq83O49zDB+Y8xrgSuBM6y1p+IHoxW7/ys+EAPOBXZYa3cGf/3eiX+Dli1r7f7wrxxr7RD+L+Ee/HHfFrzsNuAtlbnCxWeMWQv8F/ysEMFfghcDdwUvWbbjN8Y0A68m2MnCWjsRZANWzP3Hnw2oNcYkgDpgP8v4/ltrfwwcmXZ4tvt9JfAVa61nrf0F/p7A3UtzpdGbaezW2n8Ndn0B+AX+Xsfgj/1Oa+24tfZ5YAf+74jj1iz3HuBTwEfws8KhZXXvYdbxfxC4yVo7HrzmYHC8IvdfgZgfgOwuerwnOLYiGGM2AGcBDwJd1tr9wVO9+FOXy9Wn8d+E3OBxO3C06M15Of8cbAT6gFuNMY8aY24xxtSzQu6/tXYv/l/Au/ADsAH8qciVcv9Ds93vlfae+FvA94KPV8TYjTFX4pdkPD7tqRUxfuAk4EJjzIPGmB8ZY14eHK/I+BWIrWDGmAbg68AfWmsHi58LNmVflvtfGWPCeoGHK30tFZIAXgZ8wVp7FpBh2jTkMr//rfh/+W4E1gD1zDB1s5Is5/t9LMaYj+OXatxe6WtZKsaYOuBPgD+t9LVUUAJowy/N+TDwtWBWpCIUiMFeYF3R47XBsWXNGJPED8Jut9Z+Izh8IExDB/8/ONvnH+deCbzZGPMC/lT0xfg1Uy3BVBUs75+DPcAea+2DweO78AOzlXL/LwWet9b2WWuzwDfwfyZWyv0PzXa/V8R7ojHmPfhF3O8MAlFYGWPfhP9HyOPBe+Ba4BFjzGpWxvjBfw/8RjAF+5/4MyMdVGj8CsTgl8AWY8xGY0wKv1Dvngpf06IKIv9/AH5lrf2boqfuAa4LPr4O+NZSX9tSsNbeaK1da63dgH+//81a+07gh8Dbg5ct5/H3AruNMSY4dAnwDCvk/uNPSZ5vjKkL/i2E418R97/IbPf7HuDdxhjHGHM+MFA0hbksBCvlPwK82Vo7UvTUPcA1xpiaYBX1FuA/K3GNi8Va+6S1dpW1dkPwHrgHeFnwvrDs733gbuC1AMaYk4AUcIgK3f8V377CWpszxvwucB/+6qkvWWufrvBlLbZXAu8CnjTGPBYc+xPgJvwU7fXAi8DVFbq+SvkocKcx5hPAowTF7MvU7wG3B3987MRv3xBjBdx/a+2Dxpi7gEfwp6UeBb4IfIdlev+NMf8MvAboMMbsAf6M2f+9fxe/fcEO/BYG713yC47QLGO/EagB7g/+HvmFtfYD1tqnjTFfww/Mc8AN1tp8Za48GjON31o728/2srr3MOv9/xLwJWPMU/gtTK4LsqIVuf+O5624sgARERGRqqCpSREREZEKUSAmIiIiUiEKxEREREQqRIGYiIiISIUoEBMRERGpEAViIiIiIhWiQExERESkQhSIiYiIiFTI/weEUNjIkf/mJAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict_and_plot(encoder_input_data, decoder_target_data, \n",
    "                 sample_ind=70450, enc_tail_len=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict_and_plot(encoder_input_data, decoder_target_data, \n",
    "                 sample_ind=16551, enc_tail_len=100)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
